diff --git a/Fracture Detection using DL/Dataset/README.md b/Fracture Detection using DL/Dataset/README.md new file mode 100644 index 000000000..eac14850c --- /dev/null +++ b/Fracture Detection using DL/Dataset/README.md @@ -0,0 +1,12 @@ +The link for the dataset used in this project: https://www.kaggle.com/datasets/bmadushanirodrigo/fracture-multi-region-x-ray-data + +The dataset consists of 3 subdirectories under the bone_fracture_binary_classification directory, train, test and val, all three with 2 subdirectories: fracture and not-fractured; train with approximately 9200 images, val with approximately 850 and test with approximately 600 images respectively. + + +**Appropriate image count** + +Train images images: 9165 + +Validation images: 764 + +Test images: 443 \ No newline at end of file diff --git a/Fracture Detection using DL/Images/CNN Accuracy.png b/Fracture Detection using DL/Images/CNN Accuracy.png new file mode 100644 index 000000000..1bf4561ba Binary files /dev/null and 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Detection using DL/Images/Xception Loss.png b/Fracture Detection using DL/Images/Xception Loss.png new file mode 100644 index 000000000..24ea7c6d2 Binary files /dev/null and b/Fracture Detection using DL/Images/Xception Loss.png differ diff --git a/Fracture Detection using DL/Model/fracture-classify.ipynb b/Fracture Detection using DL/Model/fracture-classify.ipynb new file mode 100644 index 000000000..b1d4c5297 --- /dev/null +++ b/Fracture Detection using DL/Model/fracture-classify.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":8201044,"sourceType":"datasetVersion","datasetId":4854718}],"dockerImageVersionId":30733,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# importing modules and libraries\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport keras\nfrom keras.layers import Input, InputLayer, Conv2D, MaxPooling2D, Flatten, ELU, Dense, BatchNormalization, Activation\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import Sequential\nfrom keras.optimizers import Nadam","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-06T19:38:16.962367Z","iopub.execute_input":"2024-06-06T19:38:16.963187Z","iopub.status.idle":"2024-06-06T19:38:25.811807Z","shell.execute_reply.started":"2024-06-06T19:38:16.963149Z","shell.execute_reply":"2024-06-06T19:38:25.810776Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2024-06-06 19:38:18.573051: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-06-06 19:38:18.573146: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-06-06 19:38:18.681563: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"code","source":"from pathlib import Path\nimport imghdr\n\n# Define the data directories\ntrain_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train\"\nval_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val\"\ntest_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/test\"\n\n# Specific file types to be checked\nimage_extensions = [\".png\", \".jpg\"]\n\n# Allowed image types for TensorFlow\nimg_type_accepted_by_tf = [\"bmp\", \"gif\", \"jpeg\", \"png\"]\n\ndef check_images(directory, image_extensions, img_type_accepted_by_tf):\n for filepath in Path(directory).rglob(\"*\"):\n if filepath.suffix.lower() in image_extensions:\n img_type = imghdr.what(filepath)\n if img_type is None:\n print(f\"{filepath} is not an image\")\n elif img_type not in img_type_accepted_by_tf:\n print(f\"{filepath} is a {img_type}, not accepted by TensorFlow\")\n\n# Check images in train, validation, and test directories\nprint(\"Checking train directory...\")\ncheck_images(train_dir, image_extensions, img_type_accepted_by_tf)\n\nprint(\"\\nChecking validation directory...\")\ncheck_images(val_dir, image_extensions, img_type_accepted_by_tf)\n\nprint(\"\\nChecking test directory...\")\ncheck_images(test_dir, image_extensions, img_type_accepted_by_tf)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T11:45:54.511726Z","iopub.execute_input":"2024-06-06T11:45:54.512210Z","iopub.status.idle":"2024-06-06T11:45:59.989604Z","shell.execute_reply.started":"2024-06-06T11:45:54.512178Z","shell.execute_reply":"2024-06-06T11:45:59.988686Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Checking train directory...\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/not fractured/IMG0000505.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured/leg-xray-showing-closed-spiral-260nw-1586443063.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured/IMG0002511.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured/IMG0002447.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured/IMG0002445.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured/IMG0002436.jpg is not an image\n\nChecking validation directory...\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/not fractured/IMG0000505.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/fractured/IMG0002511.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/fractured/800px-Left_lateral_malleolus_avulsion_fracture_detail.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/fractured/IMG0002447.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/fractured/IMG0002445.jpg is not an image\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val/fractured/IMG0002436.jpg is not an image\n\nChecking test directory...\n/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/test/not fractured/IMG0000505.jpg is not an image\n","output_type":"stream"}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom collections import Counter\n\n# Function to check if an image is valid\ndef is_valid_image(image_path):\n try:\n image = tf.io.read_file(image_path)\n image = tf.image.decode_jpeg(image, channels=3)\n return True\n except:\n return False\n\n# Function to load dataset and get label counts\ndef load_dataset_and_get_label_counts(directory_path, image_size=(64, 64), batch_size=32):\n dataset = keras.utils.image_dataset_from_directory(\n directory_path,\n image_size=image_size,\n batch_size=batch_size\n )\n valid_file_paths = []\n for file_path in dataset.file_paths:\n if is_valid_image(file_path):\n valid_file_paths.append(file_path)\n \n # Recreate the dataset with only valid images\n dataset = tf.data.Dataset.from_tensor_slices(valid_file_paths)\n dataset = dataset.map(lambda x: (tf.image.resize(tf.image.decode_jpeg(tf.io.read_file(x), channels=3), image_size), tf.strings.split(x, '/')[-2]))\n labels = []\n for image, label in dataset:\n labels.append(label.numpy())\n \n label_counts = Counter(labels)\n return dataset, label_counts\n\n# Loading train dataset\ntrain_dataset, train_label_counts = load_dataset_and_get_label_counts(\n \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train\"\n)\n\n# Loading validation dataset\nval_dataset, val_label_counts = load_dataset_and_get_label_counts(\n \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val\"\n)\n\n# Loading test dataset\ntest_dataset, test_label_counts = load_dataset_and_get_label_counts(\n \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/test\"\n)\n\n# Function to format label counts\ndef format_label_counts(label_counts):\n formatted_counts = {label.decode('utf-8'): count for label, count in label_counts.items()}\n return formatted_counts\n\n# Formatting the label counts\nformatted_train_counts = format_label_counts(train_label_counts)\nformatted_val_counts = format_label_counts(val_label_counts)\nformatted_test_counts = format_label_counts(test_label_counts)\n\n# Plotting the counts\nplt.figure(figsize=(15, 5))\n\n# Train dataset\nplt.subplot(1, 3, 1)\nsns.barplot(x=list(formatted_train_counts.keys()), y=list(formatted_train_counts.values()))\nplt.title('Train Dataset')\nplt.xlabel('Class Labels')\nplt.ylabel('Image Count')\n\n# Validation dataset\nplt.subplot(1, 3, 2)\nsns.barplot(x=list(formatted_val_counts.keys()), y=list(formatted_val_counts.values()))\nplt.title('Validation Dataset')\nplt.xlabel('Class Labels')\nplt.ylabel('Image Count')\n\n# Test dataset\nplt.subplot(1, 3, 3)\nsns.barplot(x=list(formatted_test_counts.keys()), y=list(formatted_test_counts.values()))\nplt.title('Test Dataset')\nplt.xlabel('Class Labels')\nplt.ylabel('Image Count')\n\nplt.tight_layout()\nplt.show()\n\n# Printing counts\nprint(\"Train label counts:\")\nfor label, count in formatted_train_counts.items():\n print(f\"{label}: {count}\")\n\nprint(\"\\nValidation label counts:\")\nfor label, count in formatted_val_counts.items():\n print(f\"{label}: {count}\")\n\nprint(\"\\nTest label counts:\")\nfor label, count in formatted_test_counts.items():\n print(f\"{label}: {count}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T11:44:12.568846Z","iopub.execute_input":"2024-06-06T11:44:12.569229Z","iopub.status.idle":"2024-06-06T11:44:59.306858Z","shell.execute_reply.started":"2024-06-06T11:44:12.569202Z","shell.execute_reply":"2024-06-06T11:44:59.305943Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Found 9246 files belonging to 2 classes.\n","output_type":"stream"},{"name":"stderr","text":"2024-06-06 11:44:19.463746: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n2024-06-06 11:44:20.772495: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:24.677515: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:26.452550: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:30.338336: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n2024-06-06 11:44:31.091652: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n","output_type":"stream"},{"name":"stdout","text":"Found 829 files belonging to 2 classes.\n","output_type":"stream"},{"name":"stderr","text":"2024-06-06 11:44:44.382562: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:44.914222: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:45.124031: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:47.511677: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n2024-06-06 11:44:48.207115: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:48.896414: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n","output_type":"stream"},{"name":"stdout","text":"Found 506 files belonging to 2 classes.\n","output_type":"stream"},{"name":"stderr","text":"2024-06-06 11:44:53.463545: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n2024-06-06 11:44:53.813677: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n2024-06-06 11:44:54.211960: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:55.396018: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:55.618537: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n2024-06-06 11:44:56.260567: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n order = pd.unique(vector)\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n order = pd.unique(vector)\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n order = pd.unique(vector)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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label counts:\nfractured: 4606\nnot fractured: 4634\n\nValidation label counts:\nnot fractured: 486\nfractured: 337\n\nTest label counts:\nnot fractured: 262\nfractured: 238\n","output_type":"stream"}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport os\nfrom tqdm import tqdm\n\n# Function to check if an image is valid\ndef is_valid_image(image_path):\n try:\n image = tf.io.read_file(image_path)\n image = tf.image.decode_jpeg(image, channels=3)\n return True\n except:\n return False\n\n# Function to collect valid image file paths and their labels\ndef collect_valid_image_paths_and_labels(directory_path):\n valid_image_paths_and_labels = []\n for root, dirs, files in os.walk(directory_path):\n for file in tqdm(files):\n if file.lower().endswith(('.jpg', '.jpeg')):\n file_path = os.path.join(root, file)\n if is_valid_image(file_path):\n label = os.path.basename(root)\n valid_image_paths_and_labels.append((file_path, label))\n return valid_image_paths_and_labels\n\n# Function to create a dataset from valid image paths and labels\ndef create_dataset_from_valid_paths_and_labels(image_paths_and_labels, image_size=(224, 224), batch_size=32):\n # Create dataset from image paths and labels\n image_paths, labels = zip(*image_paths_and_labels)\n dataset = tf.data.Dataset.from_tensor_slices((list(image_paths), list(labels)))\n \n # Load and preprocess images\n def load_and_preprocess_image(image_path, label):\n image = tf.io.read_file(image_path)\n image = tf.image.decode_jpeg(image, channels=3)\n image = tf.image.resize(image, image_size)\n image = image / 255.0 # Normalize\n return image, label\n \n dataset = dataset.map(load_and_preprocess_image)\n \n # Create a lookup table for label encoding\n label_list = list(set(label for _, label in image_paths_and_labels))\n label_lookup = tf.lookup.StaticHashTable(\n tf.lookup.KeyValueTensorInitializer(label_list, tf.range(len(label_list))),\n default_value=-1\n )\n \n # Encode labels using the lookup table\n def encode_labels(image, label):\n label_index = label_lookup.lookup(label)\n return image, label_index\n \n dataset = dataset.map(encode_labels)\n \n # Batch the dataset\n dataset = dataset.batch(batch_size)\n \n return dataset, label_list\n\n# Function to get images and labels from a dataset\ndef get_images_and_labels(dataset):\n images_list = []\n labels_list = []\n for images, labels in dataset:\n images_list.append(images.numpy())\n labels_list.append(labels.numpy())\n return np.concatenate(images_list), np.concatenate(labels_list)\n\n# Paths to the directories\ntrain_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train\"\nval_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/val\"\ntest_dir = \"/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/test\"\n\n# Collecting valid image paths and labels\ntrain_image_paths_and_labels = collect_valid_image_paths_and_labels(train_dir)\nval_image_paths_and_labels = collect_valid_image_paths_and_labels(val_dir)\ntest_image_paths_and_labels = collect_valid_image_paths_and_labels(test_dir)\n\n# Creating datasets\ntrain_dataset, label_list = create_dataset_from_valid_paths_and_labels(train_image_paths_and_labels)\nval_dataset, _ = create_dataset_from_valid_paths_and_labels(val_image_paths_and_labels)\ntest_dataset, _ = create_dataset_from_valid_paths_and_labels(test_image_paths_and_labels)\n\n# Getting images and labels\ntrain_images, train_labels = get_images_and_labels(train_dataset)\nval_images, val_labels = get_images_and_labels(val_dataset)\ntest_images, test_labels = get_images_and_labels(test_dataset)\n\n# Printing the shapes and labels\nprint(\"Train images shape:\", train_images.shape)\nprint(\"Train labels shape:\", train_labels.shape)\nprint(\"Validation images shape:\", val_images.shape)\nprint(\"Validation labels shape:\", val_labels.shape)\nprint(\"Test images shape:\", test_images.shape)\nprint(\"Test labels shape:\", test_labels.shape)\nprint(\"Label list:\", label_list)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:38:25.813460Z","iopub.execute_input":"2024-06-06T19:38:25.814021Z","iopub.status.idle":"2024-06-06T19:39:39.068799Z","shell.execute_reply.started":"2024-06-06T19:38:25.813994Z","shell.execute_reply":"2024-06-06T19:39:39.067685Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"0it [00:00, ?it/s]\n 0%| | 1/4640 [00:00<59:31, 1.30it/s]2024-06-06 19:38:30.146858: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n 1%| | 53/4640 [00:00<00:56, 81.60it/s]2024-06-06 19:38:30.354122: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n 39%|███▉ | 1824/4640 [00:08<00:11, 244.70it/s]2024-06-06 19:38:38.203625: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n 46%|████▋ | 2154/4640 [00:10<00:10, 241.00it/s]2024-06-06 19:38:39.651862: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n 53%|█████▎ | 2445/4640 [00:11<00:10, 202.66it/s]2024-06-06 19:38:40.988858: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 414/454\n 67%|██████▋ | 3121/4640 [00:14<00:06, 252.20it/s]2024-06-06 19:38:44.142405: E tensorflow/core/lib/jpeg/jpeg_mem.cc:327] Premature end of JPEG data. Stopped at line 446/454\n100%|██████████| 4640/4640 [00:20<00:00, 222.05it/s]\n100%|██████████| 4606/4606 [00:20<00:00, 225.66it/s]\n0it [00:00, ?it/s]\n 0%| | 0/492 [00:00Model: \"functional_3\"\n\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┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m525,312\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (InputLayer)        │ (None, 224, 224, 3)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (Conv2D)           │ (None, 224, 224, 64)   │         1,792 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (Conv2D)           │ (None, 224, 224, 64)   │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (MaxPooling2D)      │ (None, 112, 112, 64)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (Conv2D)           │ (None, 112, 112, 128)  │        73,856 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (Conv2D)           │ (None, 112, 112, 128)  │       147,584 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (MaxPooling2D)      │ (None, 56, 56, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (Conv2D)           │ (None, 56, 56, 256)    │       295,168 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (MaxPooling2D)      │ (None, 28, 28, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (Conv2D)           │ (None, 28, 28, 512)    │     1,180,160 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (MaxPooling2D)      │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d        │ (None, 512)            │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (Dense)                   │ (None, 1024)           │       525,312 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout (Dropout)               │ (None, 1024)           │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (Dense)                 │ (None, 1)              │         1,025 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,161,601\u001b[0m (31.13 MB)\n","text/html":"
 Total params: 8,161,601 (31.13 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m526,337\u001b[0m (2.01 MB)\n","text/html":"
 Trainable params: 526,337 (2.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,635,264\u001b[0m (29.13 MB)\n","text/html":"
 Non-trainable params: 7,635,264 (29.13 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:19:18.254871Z","iopub.execute_input":"2024-06-06T17:19:18.255513Z","iopub.status.idle":"2024-06-06T17:28:22.373913Z","shell.execute_reply.started":"2024-06-06T17:19:18.255479Z","shell.execute_reply":"2024-06-06T17:28:22.373078Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Epoch 1/20\n","output_type":"stream"},{"name":"stderr","text":"2024-06-06 17:20:28.118048: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng11{k2=3,k3=0} for conv (f16[250,112,112,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,128]{3,2,1,0}, f16[128,3,3,128]{3,2,1,0}, f16[128]{0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:20:28.387061: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.26915269s\nTrying algorithm eng11{k2=3,k3=0} for conv (f16[250,112,112,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,128]{3,2,1,0}, f16[128,3,3,128]{3,2,1,0}, f16[128]{0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:20:38.186572: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng11{k2=3,k3=0} for conv (f16[250,112,112,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,128]{3,2,1,0}, f16[128,3,3,128]{3,2,1,0}, f16[128]{0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:20:38.474884: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.288421826s\nTrying algorithm eng11{k2=3,k3=0} for conv (f16[250,112,112,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,128]{3,2,1,0}, f16[128,3,3,128]{3,2,1,0}, f16[128]{0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1717694495.424359 106 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m36/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.5340 - loss: 1.1417","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717694596.300180 103 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - accuracy: 0.5352 - loss: 1.1342 \nEpoch 1: val_accuracy improved from -inf to 0.79581, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m238s\u001b[0m 3s/step - accuracy: 0.5363 - loss: 1.1271 - val_accuracy: 0.7958 - val_loss: 0.5429 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step - accuracy: 0.7165 - loss: 0.5658\nEpoch 2: val_accuracy did not improve from 0.79581\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 405ms/step - accuracy: 0.7170 - loss: 0.5652 - val_accuracy: 0.7592 - val_loss: 0.5313 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375ms/step - accuracy: 0.7916 - loss: 0.4776\nEpoch 3: val_accuracy did not improve from 0.79581\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 411ms/step - accuracy: 0.7916 - loss: 0.4774 - val_accuracy: 0.7343 - val_loss: 0.5218 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369ms/step - accuracy: 0.8216 - loss: 0.4265\nEpoch 4: val_accuracy improved from 0.79581 to 0.84162, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 410ms/step - accuracy: 0.8220 - loss: 0.4259 - val_accuracy: 0.8416 - val_loss: 0.3943 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.8643 - loss: 0.3678\nEpoch 5: val_accuracy improved from 0.84162 to 0.85340, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 406ms/step - accuracy: 0.8645 - loss: 0.3672 - val_accuracy: 0.8534 - val_loss: 0.3645 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.9012 - loss: 0.2908\nEpoch 6: val_accuracy improved from 0.85340 to 0.87565, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 405ms/step - accuracy: 0.9012 - loss: 0.2908 - val_accuracy: 0.8757 - val_loss: 0.3313 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step - accuracy: 0.9181 - loss: 0.2500\nEpoch 7: val_accuracy improved from 0.87565 to 0.89267, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 411ms/step - accuracy: 0.9181 - loss: 0.2499 - val_accuracy: 0.8927 - val_loss: 0.2961 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step - accuracy: 0.9372 - loss: 0.2155\nEpoch 8: val_accuracy did not improve from 0.89267\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 403ms/step - accuracy: 0.9373 - loss: 0.2153 - val_accuracy: 0.8717 - val_loss: 0.3341 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.9465 - loss: 0.1919\nEpoch 9: val_accuracy improved from 0.89267 to 0.91361, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 408ms/step - accuracy: 0.9466 - loss: 0.1915 - val_accuracy: 0.9136 - val_loss: 0.2438 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.9595 - loss: 0.1540\nEpoch 10: val_accuracy improved from 0.91361 to 0.92670, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 408ms/step - accuracy: 0.9596 - loss: 0.1538 - val_accuracy: 0.9267 - val_loss: 0.2245 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.9685 - loss: 0.1293\nEpoch 11: val_accuracy improved from 0.92670 to 0.93194, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 408ms/step - accuracy: 0.9685 - loss: 0.1292 - val_accuracy: 0.9319 - val_loss: 0.2124 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.9734 - loss: 0.1162\nEpoch 12: val_accuracy did not improve from 0.93194\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 400ms/step - accuracy: 0.9734 - loss: 0.1161 - val_accuracy: 0.9319 - val_loss: 0.2005 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - accuracy: 0.9760 - loss: 0.1038\nEpoch 13: val_accuracy improved from 0.93194 to 0.93455, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 412ms/step - accuracy: 0.9760 - loss: 0.1037 - val_accuracy: 0.9346 - val_loss: 0.1970 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 370ms/step - accuracy: 0.9824 - loss: 0.0866\nEpoch 14: val_accuracy improved from 0.93455 to 0.93717, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 411ms/step - accuracy: 0.9824 - loss: 0.0866 - val_accuracy: 0.9372 - val_loss: 0.1760 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.9840 - loss: 0.0809\nEpoch 15: val_accuracy improved from 0.93717 to 0.93848, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 407ms/step - accuracy: 0.9840 - loss: 0.0810 - val_accuracy: 0.9385 - val_loss: 0.1710 - learning_rate: 0.0010\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.9877 - loss: 0.0711\nEpoch 16: val_accuracy did not improve from 0.93848\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 401ms/step - accuracy: 0.9877 - loss: 0.0711 - val_accuracy: 0.9372 - val_loss: 0.1762 - learning_rate: 0.0010\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.9875 - loss: 0.0654\nEpoch 17: val_accuracy improved from 0.93848 to 0.94372, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 404ms/step - accuracy: 0.9875 - loss: 0.0655 - val_accuracy: 0.9437 - val_loss: 0.1638 - learning_rate: 0.0010\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step - accuracy: 0.9879 - loss: 0.0613\nEpoch 18: val_accuracy did not improve from 0.94372\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 401ms/step - accuracy: 0.9879 - loss: 0.0612 - val_accuracy: 0.9398 - val_loss: 0.1575 - learning_rate: 0.0010\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.9895 - loss: 0.0538\nEpoch 19: val_accuracy did not improve from 0.94372\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 403ms/step - accuracy: 0.9895 - loss: 0.0538 - val_accuracy: 0.9424 - val_loss: 0.1620 - learning_rate: 0.0010\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369ms/step - accuracy: 0.9898 - loss: 0.0510\nEpoch 20: val_accuracy improved from 0.94372 to 0.94634, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 409ms/step - accuracy: 0.9898 - loss: 0.0510 - val_accuracy: 0.9463 - val_loss: 0.1482 - learning_rate: 0.0010\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:28:22.375351Z","iopub.execute_input":"2024-06-06T17:28:22.375645Z","iopub.status.idle":"2024-06-06T17:28:22.619696Z","shell.execute_reply.started":"2024-06-06T17:28:22.375619Z","shell.execute_reply":"2024-06-06T17:28:22.618855Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:28:22.620915Z","iopub.execute_input":"2024-06-06T17:28:22.621273Z","iopub.status.idle":"2024-06-06T17:28:22.876228Z","shell.execute_reply.started":"2024-06-06T17:28:22.621239Z","shell.execute_reply":"2024-06-06T17:28:22.875357Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:28:22.879065Z","iopub.execute_input":"2024-06-06T17:28:22.879329Z","iopub.status.idle":"2024-06-06T17:28:59.137203Z","shell.execute_reply.started":"2024-06-06T17:28:22.879306Z","shell.execute_reply":"2024-06-06T17:28:59.136344Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 1s/step - accuracy: 0.9501 - loss: 0.1515\nTest Loss: 0.11417270451784134\nTest Accuracy: 0.9616252779960632\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### ResNet50","metadata":{}},{"cell_type":"code","source":"from keras.applications import ResNet50\nfrom keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\n# Loading model\nresnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=resnet50_model.input, outputs=resnet50_model.get_layer('conv4_block3_out').output)\n\n# Freezing convolutional layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Adding dense layers on top\nx = feature_extractor.output\nx = Flatten()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(rate=0.5)(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# binding model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\n# Setting callbacks\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1)\n\nmodel.summary() # model summary","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:42:37.281528Z","iopub.execute_input":"2024-06-06T17:42:37.281838Z","iopub.status.idle":"2024-06-06T17:42:39.542515Z","shell.execute_reply.started":"2024-06-06T17:42:37.281815Z","shell.execute_reply":"2024-06-06T17:42:39.541691Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_3\"\u001b[0m\n","text/html":"
Model: \"functional_3\"\n
\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 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200704\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m205,521,9…\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer         │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad           │ (None, 230, 230,  │          0 │ input_layer[0][0] │\n│ (ZeroPadding2D)     │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (Conv2D) │ (None, 112, 112,  │      9,472 │ conv1_pad[0][0]   │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn            │ (None, 112, 112,  │        256 │ conv1_conv[0][0]  │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu          │ (None, 112, 112,  │          0 │ conv1_bn[0][0]    │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad           │ (None, 114, 114,  │          0 │ conv1_relu[0][0]  │\n│ (ZeroPadding2D)     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool          │ (None, 56, 56,    │          0 │ pool1_pad[0][0]   │\n│ (MaxPooling2D)      │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (None, 56, 56,    │      4,160 │ pool1_pool[0][0]  │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn   │ (None, 56, 56,    │        256 │ conv2_block1_1_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (None, 56, 56,    │          0 │ conv2_block1_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block1_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn   │ (None, 56, 56,    │        256 │ conv2_block1_2_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (None, 56, 56,    │          0 │ conv2_block1_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (None, 56, 56,    │     16,640 │ pool1_pool[0][0]  │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block1_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_0_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_3_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add    │ (None, 56, 56,    │          0 │ conv2_block1_0_b… │\n│ (Add)               │ 256)              │            │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out    │ (None, 56, 56,    │          0 │ conv2_block1_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block1_out… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn   │ (None, 56, 56,    │        256 │ conv2_block2_1_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (None, 56, 56,    │          0 │ conv2_block2_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block2_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn   │ (None, 56, 56,    │        256 │ conv2_block2_2_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (None, 56, 56,    │          0 │ conv2_block2_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block2_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block2_3_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add    │ (None, 56, 56,    │          0 │ conv2_block1_out… │\n│ (Add)               │ 256)              │            │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out    │ (None, 56, 56,    │          0 │ conv2_block2_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block2_out… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn   │ (None, 56, 56,    │        256 │ conv2_block3_1_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (None, 56, 56,    │          0 │ conv2_block3_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block3_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn   │ (None, 56, 56,    │        256 │ conv2_block3_2_c… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (None, 56, 56,    │          0 │ conv2_block3_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block3_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block3_3_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add    │ (None, 56, 56,    │          0 │ conv2_block2_out… │\n│ (Add)               │ 256)              │            │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out    │ (None, 56, 56,    │          0 │ conv2_block3_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (None, 28, 28,    │     32,896 │ conv2_block3_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn   │ (None, 28, 28,    │        512 │ conv3_block1_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (None, 28, 28,    │          0 │ conv3_block1_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block1_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn   │ (None, 28, 28,    │        512 │ conv3_block1_2_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (None, 28, 28,    │          0 │ conv3_block1_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (None, 28, 28,    │    131,584 │ conv2_block3_out… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block1_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_0_c… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_3_c… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add    │ (None, 28, 28,    │          0 │ conv3_block1_0_b… │\n│ (Add)               │ 512)              │            │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out    │ (None, 28, 28,    │          0 │ conv3_block1_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block1_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn   │ (None, 28, 28,    │        512 │ conv3_block2_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (None, 28, 28,    │          0 │ conv3_block2_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block2_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn   │ (None, 28, 28,    │        512 │ conv3_block2_2_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (None, 28, 28,    │          0 │ conv3_block2_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block2_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block2_3_c… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add    │ (None, 28, 28,    │          0 │ conv3_block1_out… │\n│ (Add)               │ 512)              │            │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out    │ (None, 28, 28,    │          0 │ conv3_block2_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block2_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn   │ (None, 28, 28,    │        512 │ conv3_block3_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (None, 28, 28,    │          0 │ conv3_block3_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block3_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn   │ (None, 28, 28,    │        512 │ conv3_block3_2_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (None, 28, 28,    │          0 │ conv3_block3_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block3_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block3_3_c… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add    │ (None, 28, 28,    │          0 │ conv3_block2_out… │\n│ (Add)               │ 512)              │            │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out    │ (None, 28, 28,    │          0 │ conv3_block3_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block3_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn   │ (None, 28, 28,    │        512 │ conv3_block4_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (None, 28, 28,    │          0 │ conv3_block4_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block4_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn   │ (None, 28, 28,    │        512 │ conv3_block4_2_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (None, 28, 28,    │          0 │ conv3_block4_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block4_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block4_3_c… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add    │ (None, 28, 28,    │          0 │ conv3_block3_out… │\n│ (Add)               │ 512)              │            │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out    │ (None, 28, 28,    │          0 │ conv3_block4_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (None, 14, 14,    │    131,328 │ conv3_block4_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_1_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (None, 14, 14,    │          0 │ conv4_block1_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block1_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_2_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (None, 14, 14,    │          0 │ conv4_block1_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (None, 14, 14,    │    525,312 │ conv3_block4_out… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block1_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_0_c… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_3_c… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add    │ (None, 14, 14,    │          0 │ conv4_block1_0_b… │\n│ (Add)               │ 1024)             │            │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out    │ (None, 14, 14,    │          0 │ conv4_block1_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block1_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_1_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (None, 14, 14,    │          0 │ conv4_block2_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block2_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_2_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (None, 14, 14,    │          0 │ conv4_block2_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block2_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block2_3_c… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add    │ (None, 14, 14,    │          0 │ conv4_block1_out… │\n│ (Add)               │ 1024)             │            │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out    │ (None, 14, 14,    │          0 │ conv4_block2_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block2_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_1_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (None, 14, 14,    │          0 │ conv4_block3_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block3_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_2_c… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (None, 14, 14,    │          0 │ conv4_block3_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block3_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block3_3_c… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add    │ (None, 14, 14,    │          0 │ conv4_block2_out… │\n│ (Add)               │ 1024)             │            │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out    │ (None, 14, 14,    │          0 │ conv4_block3_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ flatten (Flatten)   │ (None, 200704)    │          0 │ conv4_block3_out… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense (Dense)       │ (None, 1024)      │ 205,521,9… │ flatten[0][0]     │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout (Dropout)   │ (None, 1024)      │          0 │ dense[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_1 (Dense)     │ (None, 1)         │      1,025 │ dropout[0][0]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m210,746,753\u001b[0m (803.94 MB)\n","text/html":"
 Total params: 210,746,753 (803.94 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m205,522,945\u001b[0m (784.01 MB)\n","text/html":"
 Trainable params: 205,522,945 (784.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,223,808\u001b[0m (19.93 MB)\n","text/html":"
 Non-trainable params: 5,223,808 (19.93 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:42:39.543813Z","iopub.execute_input":"2024-06-06T17:42:39.544418Z","iopub.status.idle":"2024-06-06T17:47:25.576460Z","shell.execute_reply.started":"2024-06-06T17:42:39.544365Z","shell.execute_reply":"2024-06-06T17:47:25.575609Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Epoch 1/20\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1717695806.509454 108 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\nW0000 00:00:1717695806.551925 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 789ms/step - accuracy: 0.4982 - loss: 7.3797","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717695835.000519 109 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717695839.463671 109 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.38743, saving model to model.keras\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717695845.491139 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 2s/step - accuracy: 0.4983 - loss: 7.3931 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - accuracy: 0.4991 - loss: 8.0737\nEpoch 2: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 243ms/step - accuracy: 0.4990 - loss: 8.0744 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - accuracy: 0.4995 - loss: 8.0666\nEpoch 3: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 246ms/step - accuracy: 0.4995 - loss: 8.0675 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - accuracy: 0.4973 - loss: 8.1020\nEpoch 4: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 245ms/step - accuracy: 0.4973 - loss: 8.1020 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - accuracy: 0.5009 - loss: 8.0453\nEpoch 5: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 248ms/step - accuracy: 0.5008 - loss: 8.0468 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - accuracy: 0.4981 - loss: 8.0896\nEpoch 6: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 6: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 250ms/step - accuracy: 0.4981 - loss: 8.0899 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - accuracy: 0.4954 - loss: 8.1340\nEpoch 7: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 254ms/step - accuracy: 0.4954 - loss: 8.1331 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-04\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 234ms/step - accuracy: 0.5060 - loss: 7.9629\nEpoch 8: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 255ms/step - accuracy: 0.5057 - loss: 7.9666 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-04\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - accuracy: 0.4910 - loss: 8.2043\nEpoch 9: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 253ms/step - accuracy: 0.4912 - loss: 8.2016 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-04\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - accuracy: 0.5009 - loss: 8.0452\nEpoch 10: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 251ms/step - accuracy: 0.5008 - loss: 8.0467 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-04\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - accuracy: 0.5032 - loss: 8.0080\nEpoch 11: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n\nEpoch 11: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 251ms/step - accuracy: 0.5030 - loss: 8.0104 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-04\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.4990 - loss: 8.0759\nEpoch 12: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 248ms/step - accuracy: 0.4989 - loss: 8.0766 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-05\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - accuracy: 0.4988 - loss: 8.0780\nEpoch 13: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 248ms/step - accuracy: 0.4988 - loss: 8.0787 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-05\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.5032 - loss: 8.0078\nEpoch 14: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 249ms/step - accuracy: 0.5030 - loss: 8.0103 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-05\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - accuracy: 0.5044 - loss: 7.9875\nEpoch 15: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 250ms/step - accuracy: 0.5043 - loss: 7.9905 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-05\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - accuracy: 0.5016 - loss: 8.0331\nEpoch 16: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n\nEpoch 16: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 250ms/step - accuracy: 0.5015 - loss: 8.0349 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-05\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 232ms/step - accuracy: 0.4917 - loss: 8.1926\nEpoch 17: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 253ms/step - accuracy: 0.4919 - loss: 8.1903 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-06\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - accuracy: 0.4954 - loss: 8.1339\nEpoch 18: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 252ms/step - accuracy: 0.4954 - loss: 8.1331 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-06\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - accuracy: 0.5026 - loss: 8.0175\nEpoch 19: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 250ms/step - accuracy: 0.5024 - loss: 8.0197 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-06\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - accuracy: 0.5081 - loss: 7.9290\nEpoch 20: val_accuracy did not improve from 0.38743\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 249ms/step - accuracy: 0.5078 - loss: 7.9335 - val_accuracy: 0.3874 - val_loss: 9.8734 - learning_rate: 1.0000e-06\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:47:25.578029Z","iopub.execute_input":"2024-06-06T17:47:25.578386Z","iopub.status.idle":"2024-06-06T17:47:25.821214Z","shell.execute_reply.started":"2024-06-06T17:47:25.578357Z","shell.execute_reply":"2024-06-06T17:47:25.820413Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:47:25.822314Z","iopub.execute_input":"2024-06-06T17:47:25.822608Z","iopub.status.idle":"2024-06-06T17:47:26.070993Z","shell.execute_reply.started":"2024-06-06T17:47:25.822584Z","shell.execute_reply":"2024-06-06T17:47:26.069978Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:47:26.072191Z","iopub.execute_input":"2024-06-06T17:47:26.072510Z","iopub.status.idle":"2024-06-06T17:48:27.188975Z","shell.execute_reply.started":"2024-06-06T17:47:26.072485Z","shell.execute_reply":"2024-06-06T17:48:27.188104Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"\u001b[1m 5/14\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 16.1181","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717696100.721273 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 495ms/step - accuracy: 0.1614 - loss: 13.5172\nTest Loss: 8.877686500549316\nTest Accuracy: 0.44920992851257324\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717696107.153840 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### DenseNet121","metadata":{}},{"cell_type":"code","source":"from keras.applications import DenseNet121\nfrom keras.layers import Dense, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\n# Loading model\ndensenet121_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=densenet121_model.input, outputs=densenet121_model.get_layer('conv5_block6_concat').output)\n\n# Freezing convolutional layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n \n# Adding dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\n\nx = Dense(1024, activation='relu')(x)\nx = Dropout(rate=0.5)(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# binding model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1)\n\nmodel.summary() # model summary","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:48:41.423294Z","iopub.execute_input":"2024-06-06T17:48:41.424030Z","iopub.status.idle":"2024-06-06T17:48:44.922549Z","shell.execute_reply.started":"2024-06-06T17:48:41.423999Z","shell.execute_reply":"2024-06-06T17:48:44.921688Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m29084464/29084464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_7\"\u001b[0m\n","text/html":"
Model: \"functional_7\"\n
\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;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ zero_padding2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m9,408\u001b[0m │ zero_padding2d[\u001b[38;5;34m0\u001b[0m… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ zero_padding2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ zero_padding2d_1… │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ pool1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv2_block1_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ conv2_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2_block1_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ conv2_block2_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ conv2_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block2_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ conv2_block3_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ conv2_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ conv2_block3_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block4_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m20,480\u001b[0m │ conv2_block4_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ conv2_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ conv2_block4_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block5_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ conv2_block5_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block4_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ conv2_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ conv2_block5_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block6_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m28,672\u001b[0m │ conv2_block6_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv2_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block5_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block6_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m32,768\u001b[0m │ pool2_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool2_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ pool2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ conv3_block1_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ conv3_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ conv3_block1_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m20,480\u001b[0m │ conv3_block2_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ conv3_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ conv3_block2_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m24,576\u001b[0m │ conv3_block3_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ conv3_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ conv3_block3_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m28,672\u001b[0m │ conv3_block4_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv3_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv3_block4_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block5_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,768\u001b[0m │ conv3_block5_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ conv3_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ conv3_block5_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block6_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block6_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block5_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m320\u001b[0m) │ │ conv3_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,280\u001b[0m │ conv3_block6_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m320\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block7_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m320\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m40,960\u001b[0m │ conv3_block7_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block7_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block7_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block7_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block6_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m352\u001b[0m) │ │ conv3_block7_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,408\u001b[0m │ conv3_block7_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m352\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block8_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m352\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m45,056\u001b[0m │ conv3_block8_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block8_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block8_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block8_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block7_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ conv3_block8_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ conv3_block8_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block9_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m49,152\u001b[0m │ conv3_block9_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block9_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block9_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block9_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block8_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m416\u001b[0m) │ │ conv3_block9_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,664\u001b[0m │ conv3_block9_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m416\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block10_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m416\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m53,248\u001b[0m │ conv3_block10_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block10_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block10_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block10_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block9_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m448\u001b[0m) │ │ conv3_block10_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,792\u001b[0m │ conv3_block10_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m448\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block11_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m448\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m57,344\u001b[0m │ conv3_block11_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block11_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block11_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block11_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block10_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ conv3_block11_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ conv3_block11_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block12_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m61,440\u001b[0m │ conv3_block12_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block12_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block12_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv3_block12_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block11_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block12_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block12_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m131,072\u001b[0m │ pool3_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool3_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ pool3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m32,768\u001b[0m │ conv4_block1_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ conv4_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ conv4_block1_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block2_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m320\u001b[0m) │ │ conv4_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,280\u001b[0m │ conv4_block2_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m320\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m320\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m40,960\u001b[0m │ conv4_block3_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m352\u001b[0m) │ │ conv4_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,408\u001b[0m │ conv4_block3_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m352\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m352\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m45,056\u001b[0m │ conv4_block4_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ conv4_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,536\u001b[0m │ conv4_block4_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m49,152\u001b[0m │ conv4_block5_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m416\u001b[0m) │ │ conv4_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,664\u001b[0m │ conv4_block5_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m416\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m416\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m53,248\u001b[0m │ conv4_block6_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m448\u001b[0m) │ │ conv4_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,792\u001b[0m │ conv4_block6_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m448\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block7_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m448\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m57,344\u001b[0m │ conv4_block7_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block7_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block7_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block7_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ conv4_block7_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ conv4_block7_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block8_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m61,440\u001b[0m │ conv4_block8_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block8_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block8_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block8_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block7_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv4_block8_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv4_block8_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block9_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m65,536\u001b[0m │ conv4_block9_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block9_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block9_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block9_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block8_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m544\u001b[0m) │ │ conv4_block9_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,176\u001b[0m │ conv4_block9_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m544\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block10_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m544\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m69,632\u001b[0m │ conv4_block10_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block10_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block10_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block10_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block9_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ conv4_block10_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,304\u001b[0m │ conv4_block10_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m576\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block11_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m576\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m73,728\u001b[0m │ conv4_block11_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block11_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block11_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block11_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block10_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m608\u001b[0m) │ │ conv4_block11_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,432\u001b[0m │ conv4_block11_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m608\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block12_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m608\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m77,824\u001b[0m │ conv4_block12_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block12_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block12_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block12_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block11_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m640\u001b[0m) │ │ conv4_block12_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,560\u001b[0m │ conv4_block12_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m640\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block13_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m640\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m81,920\u001b[0m │ conv4_block13_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block13_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block13_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block13_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block12_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m672\u001b[0m) │ │ conv4_block13_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,688\u001b[0m │ conv4_block13_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m672\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block14_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m672\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m86,016\u001b[0m │ conv4_block14_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block14_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block14_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block14_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block13_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m704\u001b[0m) │ │ conv4_block14_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,816\u001b[0m │ conv4_block14_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m704\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block15_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m704\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m90,112\u001b[0m │ conv4_block15_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block15_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block15_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block15_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block14_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m736\u001b[0m) │ │ conv4_block15_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,944\u001b[0m │ conv4_block15_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m736\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block16_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m736\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m94,208\u001b[0m │ conv4_block16_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block16_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block16_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block16_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block15_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ conv4_block16_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,072\u001b[0m │ conv4_block16_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block17_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ conv4_block17_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block17_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block17_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block17_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block16_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m800\u001b[0m) │ │ conv4_block17_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,200\u001b[0m │ conv4_block17_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m800\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block18_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m800\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m102,400\u001b[0m │ conv4_block18_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block18_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block18_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block18_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block17_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m832\u001b[0m) │ │ conv4_block18_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,328\u001b[0m │ conv4_block18_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m832\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block19_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m832\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m106,496\u001b[0m │ conv4_block19_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block19_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block19_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block19_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block18_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m864\u001b[0m) │ │ conv4_block19_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,456\u001b[0m │ conv4_block19_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m864\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block20_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m864\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m110,592\u001b[0m │ conv4_block20_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block20_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block20_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block20_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block19_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m896\u001b[0m) │ │ conv4_block20_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,584\u001b[0m │ conv4_block20_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m896\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block21_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m896\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ conv4_block21_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block21_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block21_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block21_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block20_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m928\u001b[0m) │ │ conv4_block21_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,712\u001b[0m │ conv4_block21_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m928\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block22_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m928\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m118,784\u001b[0m │ conv4_block22_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block22_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block22_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block22_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block21_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ conv4_block22_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ conv4_block22_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block23_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ conv4_block23_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block23_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block23_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block23_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block22_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m992\u001b[0m) │ │ conv4_block23_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,968\u001b[0m │ conv4_block23_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m992\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_0_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block24_0_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m992\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m126,976\u001b[0m │ conv4_block24_0_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv4_block24_1_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_re… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block24_1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_2_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m36,864\u001b[0m │ conv4_block24_1_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_conc… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block23_co… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block24_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block24_co… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool4_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m524,288\u001b[0m │ pool4_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pool4_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ pool4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,536\u001b[0m │ conv5_block1_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m544\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pool4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m544\u001b[0m) │ \u001b[38;5;34m2,176\u001b[0m │ conv5_block1_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m544\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m69,632\u001b[0m │ conv5_block2_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m2,304\u001b[0m │ conv5_block2_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m576\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,728\u001b[0m │ conv5_block3_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m608\u001b[0m) │ \u001b[38;5;34m2,432\u001b[0m │ conv5_block3_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block4_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m77,824\u001b[0m │ conv5_block4_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,560\u001b[0m │ conv5_block4_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block5_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m81,920\u001b[0m │ conv5_block5_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m672\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block4_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m672\u001b[0m) │ \u001b[38;5;34m2,688\u001b[0m │ conv5_block5_con… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_0_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m672\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block6_0_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m86,016\u001b[0m │ conv5_block6_0_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv5_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m36,864\u001b[0m │ conv5_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_concat │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m704\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block5_con… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ conv5_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m704\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block6_con… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m721,920\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_1       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ zero_padding2d      │ (None, 230, 230,  │          0 │ input_layer_1[0]… │\n│ (ZeroPadding2D)     │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (Conv2D) │ (None, 112, 112,  │      9,408 │ zero_padding2d[0… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn            │ (None, 112, 112,  │        256 │ conv1_conv[0][0]  │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu          │ (None, 112, 112,  │          0 │ conv1_bn[0][0]    │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ zero_padding2d_1    │ (None, 114, 114,  │          0 │ conv1_relu[0][0]  │\n│ (ZeroPadding2D)     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1               │ (None, 56, 56,    │          0 │ zero_padding2d_1… │\n│ (MaxPooling2D)      │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn   │ (None, 56, 56,    │        256 │ pool1[0][0]       │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_relu │ (None, 56, 56,    │          0 │ conv2_block1_0_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (None, 56, 56,    │      8,192 │ conv2_block1_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn   │ (None, 56, 56,    │        512 │ conv2_block1_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (None, 56, 56,    │          0 │ conv2_block1_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block1_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_concat │ (None, 56, 56,    │          0 │ pool1[0][0],      │\n│ (Concatenate)       │ 96)               │            │ conv2_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_0_bn   │ (None, 56, 56,    │        384 │ conv2_block1_con… │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_0_relu │ (None, 56, 56,    │          0 │ conv2_block2_0_b… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (None, 56, 56,    │     12,288 │ conv2_block2_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn   │ (None, 56, 56,    │        512 │ conv2_block2_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (None, 56, 56,    │          0 │ conv2_block2_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block2_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_concat │ (None, 56, 56,    │          0 │ conv2_block1_con… │\n│ (Concatenate)       │ 128)              │            │ conv2_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_0_bn   │ (None, 56, 56,    │        512 │ conv2_block2_con… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_0_relu │ (None, 56, 56,    │          0 │ conv2_block3_0_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (None, 56, 56,    │     16,384 │ conv2_block3_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn   │ (None, 56, 56,    │        512 │ conv2_block3_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (None, 56, 56,    │          0 │ conv2_block3_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block3_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_concat │ (None, 56, 56,    │          0 │ conv2_block2_con… │\n│ (Concatenate)       │ 160)              │            │ conv2_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_0_bn   │ (None, 56, 56,    │        640 │ conv2_block3_con… │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_0_relu │ (None, 56, 56,    │          0 │ conv2_block4_0_b… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_conv │ (None, 56, 56,    │     20,480 │ conv2_block4_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_bn   │ (None, 56, 56,    │        512 │ conv2_block4_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_1_relu │ (None, 56, 56,    │          0 │ conv2_block4_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block4_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block4_concat │ (None, 56, 56,    │          0 │ conv2_block3_con… │\n│ (Concatenate)       │ 192)              │            │ conv2_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_0_bn   │ (None, 56, 56,    │        768 │ conv2_block4_con… │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_0_relu │ (None, 56, 56,    │          0 │ conv2_block5_0_b… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_conv │ (None, 56, 56,    │     24,576 │ conv2_block5_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_bn   │ (None, 56, 56,    │        512 │ conv2_block5_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_1_relu │ (None, 56, 56,    │          0 │ conv2_block5_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block5_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block5_concat │ (None, 56, 56,    │          0 │ conv2_block4_con… │\n│ (Concatenate)       │ 224)              │            │ conv2_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_0_bn   │ (None, 56, 56,    │        896 │ conv2_block5_con… │\n│ (BatchNormalizatio… │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_0_relu │ (None, 56, 56,    │          0 │ conv2_block6_0_b… │\n│ (Activation)        │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_conv │ (None, 56, 56,    │     28,672 │ conv2_block6_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_bn   │ (None, 56, 56,    │        512 │ conv2_block6_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_1_relu │ (None, 56, 56,    │          0 │ conv2_block6_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_2_conv │ (None, 56, 56,    │     36,864 │ conv2_block6_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block6_concat │ (None, 56, 56,    │          0 │ conv2_block5_con… │\n│ (Concatenate)       │ 256)              │            │ conv2_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_bn            │ (None, 56, 56,    │      1,024 │ conv2_block6_con… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_relu          │ (None, 56, 56,    │          0 │ pool2_bn[0][0]    │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_conv (Conv2D) │ (None, 56, 56,    │     32,768 │ pool2_relu[0][0]  │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool2_pool          │ (None, 28, 28,    │          0 │ pool2_conv[0][0]  │\n│ (AveragePooling2D)  │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn   │ (None, 28, 28,    │        512 │ pool2_pool[0][0]  │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_relu │ (None, 28, 28,    │          0 │ conv3_block1_0_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (None, 28, 28,    │     16,384 │ conv3_block1_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn   │ (None, 28, 28,    │        512 │ conv3_block1_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (None, 28, 28,    │          0 │ conv3_block1_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block1_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_concat │ (None, 28, 28,    │          0 │ pool2_pool[0][0], │\n│ (Concatenate)       │ 160)              │            │ conv3_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_0_bn   │ (None, 28, 28,    │        640 │ conv3_block1_con… │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_0_relu │ (None, 28, 28,    │          0 │ conv3_block2_0_b… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (None, 28, 28,    │     20,480 │ conv3_block2_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn   │ (None, 28, 28,    │        512 │ conv3_block2_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (None, 28, 28,    │          0 │ conv3_block2_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block2_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_concat │ (None, 28, 28,    │          0 │ conv3_block1_con… │\n│ (Concatenate)       │ 192)              │            │ conv3_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_0_bn   │ (None, 28, 28,    │        768 │ conv3_block2_con… │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_0_relu │ (None, 28, 28,    │          0 │ conv3_block3_0_b… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (None, 28, 28,    │     24,576 │ conv3_block3_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn   │ (None, 28, 28,    │        512 │ conv3_block3_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (None, 28, 28,    │          0 │ conv3_block3_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block3_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_concat │ (None, 28, 28,    │          0 │ conv3_block2_con… │\n│ (Concatenate)       │ 224)              │            │ conv3_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_0_bn   │ (None, 28, 28,    │        896 │ conv3_block3_con… │\n│ (BatchNormalizatio… │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_0_relu │ (None, 28, 28,    │          0 │ conv3_block4_0_b… │\n│ (Activation)        │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (None, 28, 28,    │     28,672 │ conv3_block4_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn   │ (None, 28, 28,    │        512 │ conv3_block4_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (None, 28, 28,    │          0 │ conv3_block4_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block4_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_concat │ (None, 28, 28,    │          0 │ conv3_block3_con… │\n│ (Concatenate)       │ 256)              │            │ conv3_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_0_bn   │ (None, 28, 28,    │      1,024 │ conv3_block4_con… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_0_relu │ (None, 28, 28,    │          0 │ conv3_block5_0_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_conv │ (None, 28, 28,    │     32,768 │ conv3_block5_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_bn   │ (None, 28, 28,    │        512 │ conv3_block5_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_1_relu │ (None, 28, 28,    │          0 │ conv3_block5_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block5_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block5_concat │ (None, 28, 28,    │          0 │ conv3_block4_con… │\n│ (Concatenate)       │ 288)              │            │ conv3_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_0_bn   │ (None, 28, 28,    │      1,152 │ conv3_block5_con… │\n│ (BatchNormalizatio… │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_0_relu │ (None, 28, 28,    │          0 │ conv3_block6_0_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_conv │ (None, 28, 28,    │     36,864 │ conv3_block6_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_bn   │ (None, 28, 28,    │        512 │ conv3_block6_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_1_relu │ (None, 28, 28,    │          0 │ conv3_block6_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block6_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block6_concat │ (None, 28, 28,    │          0 │ conv3_block5_con… │\n│ (Concatenate)       │ 320)              │            │ conv3_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_0_bn   │ (None, 28, 28,    │      1,280 │ conv3_block6_con… │\n│ (BatchNormalizatio… │ 320)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_0_relu │ (None, 28, 28,    │          0 │ conv3_block7_0_b… │\n│ (Activation)        │ 320)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_conv │ (None, 28, 28,    │     40,960 │ conv3_block7_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_bn   │ (None, 28, 28,    │        512 │ conv3_block7_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_1_relu │ (None, 28, 28,    │          0 │ conv3_block7_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block7_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block7_concat │ (None, 28, 28,    │          0 │ conv3_block6_con… │\n│ (Concatenate)       │ 352)              │            │ conv3_block7_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_0_bn   │ (None, 28, 28,    │      1,408 │ conv3_block7_con… │\n│ (BatchNormalizatio… │ 352)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_0_relu │ (None, 28, 28,    │          0 │ conv3_block8_0_b… │\n│ (Activation)        │ 352)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_conv │ (None, 28, 28,    │     45,056 │ conv3_block8_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_bn   │ (None, 28, 28,    │        512 │ conv3_block8_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_1_relu │ (None, 28, 28,    │          0 │ conv3_block8_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block8_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block8_concat │ (None, 28, 28,    │          0 │ conv3_block7_con… │\n│ (Concatenate)       │ 384)              │            │ conv3_block8_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_0_bn   │ (None, 28, 28,    │      1,536 │ conv3_block8_con… │\n│ (BatchNormalizatio… │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_0_relu │ (None, 28, 28,    │          0 │ conv3_block9_0_b… │\n│ (Activation)        │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_conv │ (None, 28, 28,    │     49,152 │ conv3_block9_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_bn   │ (None, 28, 28,    │        512 │ conv3_block9_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_1_relu │ (None, 28, 28,    │          0 │ conv3_block9_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_2_conv │ (None, 28, 28,    │     36,864 │ conv3_block9_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block9_concat │ (None, 28, 28,    │          0 │ conv3_block8_con… │\n│ (Concatenate)       │ 416)              │            │ conv3_block9_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_0_bn  │ (None, 28, 28,    │      1,664 │ conv3_block9_con… │\n│ (BatchNormalizatio… │ 416)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_0_re… │ (None, 28, 28,    │          0 │ conv3_block10_0_… │\n│ (Activation)        │ 416)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_co… │ (None, 28, 28,    │     53,248 │ conv3_block10_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_bn  │ (None, 28, 28,    │        512 │ conv3_block10_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_1_re… │ (None, 28, 28,    │          0 │ conv3_block10_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_2_co… │ (None, 28, 28,    │     36,864 │ conv3_block10_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block10_conc… │ (None, 28, 28,    │          0 │ conv3_block9_con… │\n│ (Concatenate)       │ 448)              │            │ conv3_block10_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_0_bn  │ (None, 28, 28,    │      1,792 │ conv3_block10_co… │\n│ (BatchNormalizatio… │ 448)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_0_re… │ (None, 28, 28,    │          0 │ conv3_block11_0_… │\n│ (Activation)        │ 448)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_co… │ (None, 28, 28,    │     57,344 │ conv3_block11_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_bn  │ (None, 28, 28,    │        512 │ conv3_block11_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_1_re… │ (None, 28, 28,    │          0 │ conv3_block11_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_2_co… │ (None, 28, 28,    │     36,864 │ conv3_block11_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block11_conc… │ (None, 28, 28,    │          0 │ conv3_block10_co… │\n│ (Concatenate)       │ 480)              │            │ conv3_block11_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_0_bn  │ (None, 28, 28,    │      1,920 │ conv3_block11_co… │\n│ (BatchNormalizatio… │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_0_re… │ (None, 28, 28,    │          0 │ conv3_block12_0_… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_co… │ (None, 28, 28,    │     61,440 │ conv3_block12_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_bn  │ (None, 28, 28,    │        512 │ conv3_block12_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_1_re… │ (None, 28, 28,    │          0 │ conv3_block12_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_2_co… │ (None, 28, 28,    │     36,864 │ conv3_block12_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block12_conc… │ (None, 28, 28,    │          0 │ conv3_block11_co… │\n│ (Concatenate)       │ 512)              │            │ conv3_block12_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_bn            │ (None, 28, 28,    │      2,048 │ conv3_block12_co… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_relu          │ (None, 28, 28,    │          0 │ pool3_bn[0][0]    │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_conv (Conv2D) │ (None, 28, 28,    │    131,072 │ pool3_relu[0][0]  │\n│                     │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool3_pool          │ (None, 14, 14,    │          0 │ pool3_conv[0][0]  │\n│ (AveragePooling2D)  │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn   │ (None, 14, 14,    │      1,024 │ pool3_pool[0][0]  │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_relu │ (None, 14, 14,    │          0 │ conv4_block1_0_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (None, 14, 14,    │     32,768 │ conv4_block1_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn   │ (None, 14, 14,    │        512 │ conv4_block1_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (None, 14, 14,    │          0 │ conv4_block1_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block1_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_concat │ (None, 14, 14,    │          0 │ pool3_pool[0][0], │\n│ (Concatenate)       │ 288)              │            │ conv4_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_0_bn   │ (None, 14, 14,    │      1,152 │ conv4_block1_con… │\n│ (BatchNormalizatio… │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_0_relu │ (None, 14, 14,    │          0 │ conv4_block2_0_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (None, 14, 14,    │     36,864 │ conv4_block2_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn   │ (None, 14, 14,    │        512 │ conv4_block2_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (None, 14, 14,    │          0 │ conv4_block2_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block2_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_concat │ (None, 14, 14,    │          0 │ conv4_block1_con… │\n│ (Concatenate)       │ 320)              │            │ conv4_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_0_bn   │ (None, 14, 14,    │      1,280 │ conv4_block2_con… │\n│ (BatchNormalizatio… │ 320)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_0_relu │ (None, 14, 14,    │          0 │ conv4_block3_0_b… │\n│ (Activation)        │ 320)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (None, 14, 14,    │     40,960 │ conv4_block3_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn   │ (None, 14, 14,    │        512 │ conv4_block3_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (None, 14, 14,    │          0 │ conv4_block3_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block3_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_concat │ (None, 14, 14,    │          0 │ conv4_block2_con… │\n│ (Concatenate)       │ 352)              │            │ conv4_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_0_bn   │ (None, 14, 14,    │      1,408 │ conv4_block3_con… │\n│ (BatchNormalizatio… │ 352)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_0_relu │ (None, 14, 14,    │          0 │ conv4_block4_0_b… │\n│ (Activation)        │ 352)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (None, 14, 14,    │     45,056 │ conv4_block4_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn   │ (None, 14, 14,    │        512 │ conv4_block4_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (None, 14, 14,    │          0 │ conv4_block4_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block4_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_concat │ (None, 14, 14,    │          0 │ conv4_block3_con… │\n│ (Concatenate)       │ 384)              │            │ conv4_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_0_bn   │ (None, 14, 14,    │      1,536 │ conv4_block4_con… │\n│ (BatchNormalizatio… │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_0_relu │ (None, 14, 14,    │          0 │ conv4_block5_0_b… │\n│ (Activation)        │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (None, 14, 14,    │     49,152 │ conv4_block5_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn   │ (None, 14, 14,    │        512 │ conv4_block5_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (None, 14, 14,    │          0 │ conv4_block5_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block5_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_concat │ (None, 14, 14,    │          0 │ conv4_block4_con… │\n│ (Concatenate)       │ 416)              │            │ conv4_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_0_bn   │ (None, 14, 14,    │      1,664 │ conv4_block5_con… │\n│ (BatchNormalizatio… │ 416)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_0_relu │ (None, 14, 14,    │          0 │ conv4_block6_0_b… │\n│ (Activation)        │ 416)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (None, 14, 14,    │     53,248 │ conv4_block6_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn   │ (None, 14, 14,    │        512 │ conv4_block6_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (None, 14, 14,    │          0 │ conv4_block6_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block6_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_concat │ (None, 14, 14,    │          0 │ conv4_block5_con… │\n│ (Concatenate)       │ 448)              │            │ conv4_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_0_bn   │ (None, 14, 14,    │      1,792 │ conv4_block6_con… │\n│ (BatchNormalizatio… │ 448)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_0_relu │ (None, 14, 14,    │          0 │ conv4_block7_0_b… │\n│ (Activation)        │ 448)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_conv │ (None, 14, 14,    │     57,344 │ conv4_block7_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_bn   │ (None, 14, 14,    │        512 │ conv4_block7_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_1_relu │ (None, 14, 14,    │          0 │ conv4_block7_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block7_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block7_concat │ (None, 14, 14,    │          0 │ conv4_block6_con… │\n│ (Concatenate)       │ 480)              │            │ conv4_block7_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_0_bn   │ (None, 14, 14,    │      1,920 │ conv4_block7_con… │\n│ (BatchNormalizatio… │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_0_relu │ (None, 14, 14,    │          0 │ conv4_block8_0_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_conv │ (None, 14, 14,    │     61,440 │ conv4_block8_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_bn   │ (None, 14, 14,    │        512 │ conv4_block8_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_1_relu │ (None, 14, 14,    │          0 │ conv4_block8_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block8_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block8_concat │ (None, 14, 14,    │          0 │ conv4_block7_con… │\n│ (Concatenate)       │ 512)              │            │ conv4_block8_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_0_bn   │ (None, 14, 14,    │      2,048 │ conv4_block8_con… │\n│ (BatchNormalizatio… │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_0_relu │ (None, 14, 14,    │          0 │ conv4_block9_0_b… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_conv │ (None, 14, 14,    │     65,536 │ conv4_block9_0_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_bn   │ (None, 14, 14,    │        512 │ conv4_block9_1_c… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_1_relu │ (None, 14, 14,    │          0 │ conv4_block9_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_2_conv │ (None, 14, 14,    │     36,864 │ conv4_block9_1_r… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block9_concat │ (None, 14, 14,    │          0 │ conv4_block8_con… │\n│ (Concatenate)       │ 544)              │            │ conv4_block9_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_0_bn  │ (None, 14, 14,    │      2,176 │ conv4_block9_con… │\n│ (BatchNormalizatio… │ 544)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_0_re… │ (None, 14, 14,    │          0 │ conv4_block10_0_… │\n│ (Activation)        │ 544)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_co… │ (None, 14, 14,    │     69,632 │ conv4_block10_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_bn  │ (None, 14, 14,    │        512 │ conv4_block10_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_1_re… │ (None, 14, 14,    │          0 │ conv4_block10_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block10_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block10_conc… │ (None, 14, 14,    │          0 │ conv4_block9_con… │\n│ (Concatenate)       │ 576)              │            │ conv4_block10_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_0_bn  │ (None, 14, 14,    │      2,304 │ conv4_block10_co… │\n│ (BatchNormalizatio… │ 576)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_0_re… │ (None, 14, 14,    │          0 │ conv4_block11_0_… │\n│ (Activation)        │ 576)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_co… │ (None, 14, 14,    │     73,728 │ conv4_block11_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_bn  │ (None, 14, 14,    │        512 │ conv4_block11_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_1_re… │ (None, 14, 14,    │          0 │ conv4_block11_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block11_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block11_conc… │ (None, 14, 14,    │          0 │ conv4_block10_co… │\n│ (Concatenate)       │ 608)              │            │ conv4_block11_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_0_bn  │ (None, 14, 14,    │      2,432 │ conv4_block11_co… │\n│ (BatchNormalizatio… │ 608)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_0_re… │ (None, 14, 14,    │          0 │ conv4_block12_0_… │\n│ (Activation)        │ 608)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_co… │ (None, 14, 14,    │     77,824 │ conv4_block12_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_bn  │ (None, 14, 14,    │        512 │ conv4_block12_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_1_re… │ (None, 14, 14,    │          0 │ conv4_block12_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block12_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block12_conc… │ (None, 14, 14,    │          0 │ conv4_block11_co… │\n│ (Concatenate)       │ 640)              │            │ conv4_block12_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_0_bn  │ (None, 14, 14,    │      2,560 │ conv4_block12_co… │\n│ (BatchNormalizatio… │ 640)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_0_re… │ (None, 14, 14,    │          0 │ conv4_block13_0_… │\n│ (Activation)        │ 640)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_co… │ (None, 14, 14,    │     81,920 │ conv4_block13_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_bn  │ (None, 14, 14,    │        512 │ conv4_block13_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_1_re… │ (None, 14, 14,    │          0 │ conv4_block13_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block13_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block13_conc… │ (None, 14, 14,    │          0 │ conv4_block12_co… │\n│ (Concatenate)       │ 672)              │            │ conv4_block13_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_0_bn  │ (None, 14, 14,    │      2,688 │ conv4_block13_co… │\n│ (BatchNormalizatio… │ 672)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_0_re… │ (None, 14, 14,    │          0 │ conv4_block14_0_… │\n│ (Activation)        │ 672)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_co… │ (None, 14, 14,    │     86,016 │ conv4_block14_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_bn  │ (None, 14, 14,    │        512 │ conv4_block14_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_1_re… │ (None, 14, 14,    │          0 │ conv4_block14_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block14_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block14_conc… │ (None, 14, 14,    │          0 │ conv4_block13_co… │\n│ (Concatenate)       │ 704)              │            │ conv4_block14_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_0_bn  │ (None, 14, 14,    │      2,816 │ conv4_block14_co… │\n│ (BatchNormalizatio… │ 704)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_0_re… │ (None, 14, 14,    │          0 │ conv4_block15_0_… │\n│ (Activation)        │ 704)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_co… │ (None, 14, 14,    │     90,112 │ conv4_block15_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_bn  │ (None, 14, 14,    │        512 │ conv4_block15_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_1_re… │ (None, 14, 14,    │          0 │ conv4_block15_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block15_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block15_conc… │ (None, 14, 14,    │          0 │ conv4_block14_co… │\n│ (Concatenate)       │ 736)              │            │ conv4_block15_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_0_bn  │ (None, 14, 14,    │      2,944 │ conv4_block15_co… │\n│ (BatchNormalizatio… │ 736)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_0_re… │ (None, 14, 14,    │          0 │ conv4_block16_0_… │\n│ (Activation)        │ 736)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_co… │ (None, 14, 14,    │     94,208 │ conv4_block16_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_bn  │ (None, 14, 14,    │        512 │ conv4_block16_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_1_re… │ (None, 14, 14,    │          0 │ conv4_block16_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block16_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block16_conc… │ (None, 14, 14,    │          0 │ conv4_block15_co… │\n│ (Concatenate)       │ 768)              │            │ conv4_block16_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_0_bn  │ (None, 14, 14,    │      3,072 │ conv4_block16_co… │\n│ (BatchNormalizatio… │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_0_re… │ (None, 14, 14,    │          0 │ conv4_block17_0_… │\n│ (Activation)        │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_co… │ (None, 14, 14,    │     98,304 │ conv4_block17_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_bn  │ (None, 14, 14,    │        512 │ conv4_block17_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_1_re… │ (None, 14, 14,    │          0 │ conv4_block17_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block17_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block17_conc… │ (None, 14, 14,    │          0 │ conv4_block16_co… │\n│ (Concatenate)       │ 800)              │            │ conv4_block17_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_0_bn  │ (None, 14, 14,    │      3,200 │ conv4_block17_co… │\n│ (BatchNormalizatio… │ 800)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_0_re… │ (None, 14, 14,    │          0 │ conv4_block18_0_… │\n│ (Activation)        │ 800)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_co… │ (None, 14, 14,    │    102,400 │ conv4_block18_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_bn  │ (None, 14, 14,    │        512 │ conv4_block18_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_1_re… │ (None, 14, 14,    │          0 │ conv4_block18_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block18_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block18_conc… │ (None, 14, 14,    │          0 │ conv4_block17_co… │\n│ (Concatenate)       │ 832)              │            │ conv4_block18_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_0_bn  │ (None, 14, 14,    │      3,328 │ conv4_block18_co… │\n│ (BatchNormalizatio… │ 832)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_0_re… │ (None, 14, 14,    │          0 │ conv4_block19_0_… │\n│ (Activation)        │ 832)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_co… │ (None, 14, 14,    │    106,496 │ conv4_block19_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_bn  │ (None, 14, 14,    │        512 │ conv4_block19_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_1_re… │ (None, 14, 14,    │          0 │ conv4_block19_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block19_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block19_conc… │ (None, 14, 14,    │          0 │ conv4_block18_co… │\n│ (Concatenate)       │ 864)              │            │ conv4_block19_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_0_bn  │ (None, 14, 14,    │      3,456 │ conv4_block19_co… │\n│ (BatchNormalizatio… │ 864)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_0_re… │ (None, 14, 14,    │          0 │ conv4_block20_0_… │\n│ (Activation)        │ 864)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_co… │ (None, 14, 14,    │    110,592 │ conv4_block20_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_bn  │ (None, 14, 14,    │        512 │ conv4_block20_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_1_re… │ (None, 14, 14,    │          0 │ conv4_block20_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block20_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block20_conc… │ (None, 14, 14,    │          0 │ conv4_block19_co… │\n│ (Concatenate)       │ 896)              │            │ conv4_block20_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_0_bn  │ (None, 14, 14,    │      3,584 │ conv4_block20_co… │\n│ (BatchNormalizatio… │ 896)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_0_re… │ (None, 14, 14,    │          0 │ conv4_block21_0_… │\n│ (Activation)        │ 896)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_co… │ (None, 14, 14,    │    114,688 │ conv4_block21_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_bn  │ (None, 14, 14,    │        512 │ conv4_block21_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_1_re… │ (None, 14, 14,    │          0 │ conv4_block21_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block21_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block21_conc… │ (None, 14, 14,    │          0 │ conv4_block20_co… │\n│ (Concatenate)       │ 928)              │            │ conv4_block21_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_0_bn  │ (None, 14, 14,    │      3,712 │ conv4_block21_co… │\n│ (BatchNormalizatio… │ 928)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_0_re… │ (None, 14, 14,    │          0 │ conv4_block22_0_… │\n│ (Activation)        │ 928)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_co… │ (None, 14, 14,    │    118,784 │ conv4_block22_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_bn  │ (None, 14, 14,    │        512 │ conv4_block22_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_1_re… │ (None, 14, 14,    │          0 │ conv4_block22_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block22_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block22_conc… │ (None, 14, 14,    │          0 │ conv4_block21_co… │\n│ (Concatenate)       │ 960)              │            │ conv4_block22_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_0_bn  │ (None, 14, 14,    │      3,840 │ conv4_block22_co… │\n│ (BatchNormalizatio… │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_0_re… │ (None, 14, 14,    │          0 │ conv4_block23_0_… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_co… │ (None, 14, 14,    │    122,880 │ conv4_block23_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_bn  │ (None, 14, 14,    │        512 │ conv4_block23_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_1_re… │ (None, 14, 14,    │          0 │ conv4_block23_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block23_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block23_conc… │ (None, 14, 14,    │          0 │ conv4_block22_co… │\n│ (Concatenate)       │ 992)              │            │ conv4_block23_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_0_bn  │ (None, 14, 14,    │      3,968 │ conv4_block23_co… │\n│ (BatchNormalizatio… │ 992)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_0_re… │ (None, 14, 14,    │          0 │ conv4_block24_0_… │\n│ (Activation)        │ 992)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_co… │ (None, 14, 14,    │    126,976 │ conv4_block24_0_… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_bn  │ (None, 14, 14,    │        512 │ conv4_block24_1_… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_1_re… │ (None, 14, 14,    │          0 │ conv4_block24_1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_2_co… │ (None, 14, 14,    │     36,864 │ conv4_block24_1_… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block24_conc… │ (None, 14, 14,    │          0 │ conv4_block23_co… │\n│ (Concatenate)       │ 1024)             │            │ conv4_block24_2_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_bn            │ (None, 14, 14,    │      4,096 │ conv4_block24_co… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_relu          │ (None, 14, 14,    │          0 │ pool4_bn[0][0]    │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_conv (Conv2D) │ (None, 14, 14,    │    524,288 │ pool4_relu[0][0]  │\n│                     │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool4_pool          │ (None, 7, 7, 512) │          0 │ pool4_conv[0][0]  │\n│ (AveragePooling2D)  │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn   │ (None, 7, 7, 512) │      2,048 │ pool4_pool[0][0]  │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (None, 7, 7, 128) │     65,536 │ conv5_block1_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block1_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block1_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block1_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_concat │ (None, 7, 7, 544) │          0 │ pool4_pool[0][0], │\n│ (Concatenate)       │                   │            │ conv5_block1_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_0_bn   │ (None, 7, 7, 544) │      2,176 │ conv5_block1_con… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_0_relu │ (None, 7, 7, 544) │          0 │ conv5_block2_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (None, 7, 7, 128) │     69,632 │ conv5_block2_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block2_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block2_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block2_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_concat │ (None, 7, 7, 576) │          0 │ conv5_block1_con… │\n│ (Concatenate)       │                   │            │ conv5_block2_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_0_bn   │ (None, 7, 7, 576) │      2,304 │ conv5_block2_con… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_0_relu │ (None, 7, 7, 576) │          0 │ conv5_block3_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (None, 7, 7, 128) │     73,728 │ conv5_block3_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block3_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block3_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block3_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_concat │ (None, 7, 7, 608) │          0 │ conv5_block2_con… │\n│ (Concatenate)       │                   │            │ conv5_block3_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_0_bn   │ (None, 7, 7, 608) │      2,432 │ conv5_block3_con… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_0_relu │ (None, 7, 7, 608) │          0 │ conv5_block4_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_conv │ (None, 7, 7, 128) │     77,824 │ conv5_block4_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block4_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block4_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block4_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block4_concat │ (None, 7, 7, 640) │          0 │ conv5_block3_con… │\n│ (Concatenate)       │                   │            │ conv5_block4_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_0_bn   │ (None, 7, 7, 640) │      2,560 │ conv5_block4_con… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_0_relu │ (None, 7, 7, 640) │          0 │ conv5_block5_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_conv │ (None, 7, 7, 128) │     81,920 │ conv5_block5_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block5_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block5_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block5_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block5_concat │ (None, 7, 7, 672) │          0 │ conv5_block4_con… │\n│ (Concatenate)       │                   │            │ conv5_block5_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_0_bn   │ (None, 7, 7, 672) │      2,688 │ conv5_block5_con… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_0_relu │ (None, 7, 7, 672) │          0 │ conv5_block6_0_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_conv │ (None, 7, 7, 128) │     86,016 │ conv5_block6_0_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_bn   │ (None, 7, 7, 128) │        512 │ conv5_block6_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_1_relu │ (None, 7, 7, 128) │          0 │ conv5_block6_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_2_conv │ (None, 7, 7, 32)  │     36,864 │ conv5_block6_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block6_concat │ (None, 7, 7, 704) │          0 │ conv5_block5_con… │\n│ (Concatenate)       │                   │            │ conv5_block6_2_c… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 704)       │          0 │ conv5_block6_con… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_2 (Dense)     │ (None, 1024)      │    721,920 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_1 (Dropout) │ (None, 1024)      │          0 │ dense_2[0][0]     │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_3 (Dense)     │ (None, 1)         │      1,025 │ dropout_1[0][0]   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,263,233\u001b[0m (23.89 MB)\n","text/html":"
 Total params: 6,263,233 (23.89 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m722,945\u001b[0m (2.76 MB)\n","text/html":"
 Trainable params: 722,945 (2.76 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,540,288\u001b[0m (21.13 MB)\n","text/html":"
 Non-trainable params: 5,540,288 (21.13 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:48:45.613312Z","iopub.execute_input":"2024-06-06T17:48:45.614105Z","iopub.status.idle":"2024-06-06T17:55:10.284281Z","shell.execute_reply.started":"2024-06-06T17:48:45.614074Z","shell.execute_reply":"2024-06-06T17:55:10.283444Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Epoch 1/20\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717696210.506478 109 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.6642 - loss: 0.6293 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717696264.544011 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717696274.190563 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.80366, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 2s/step - accuracy: 0.6654 - loss: 0.6281 - val_accuracy: 0.8037 - val_loss: 0.4606 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.7794 - loss: 0.4734\nEpoch 2: val_accuracy improved from 0.80366 to 0.81937, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 310ms/step - accuracy: 0.7796 - loss: 0.4731 - val_accuracy: 0.8194 - val_loss: 0.4175 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - accuracy: 0.8300 - loss: 0.3951\nEpoch 3: val_accuracy improved from 0.81937 to 0.84817, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 314ms/step - accuracy: 0.8299 - loss: 0.3949 - val_accuracy: 0.8482 - val_loss: 0.3553 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 269ms/step - accuracy: 0.8613 - loss: 0.3436\nEpoch 4: val_accuracy improved from 0.84817 to 0.85079, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 317ms/step - accuracy: 0.8613 - loss: 0.3434 - val_accuracy: 0.8508 - val_loss: 0.3163 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 263ms/step - accuracy: 0.8748 - loss: 0.3130\nEpoch 5: val_accuracy improved from 0.85079 to 0.86911, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 311ms/step - accuracy: 0.8751 - loss: 0.3126 - val_accuracy: 0.8691 - val_loss: 0.2924 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 260ms/step - accuracy: 0.8901 - loss: 0.2817\nEpoch 6: val_accuracy improved from 0.86911 to 0.89136, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 307ms/step - accuracy: 0.8904 - loss: 0.2813 - val_accuracy: 0.8914 - val_loss: 0.2771 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - accuracy: 0.9131 - loss: 0.2422\nEpoch 7: val_accuracy did not improve from 0.89136\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 286ms/step - accuracy: 0.9133 - loss: 0.2420 - val_accuracy: 0.8757 - val_loss: 0.2587 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - accuracy: 0.9242 - loss: 0.2202\nEpoch 8: val_accuracy improved from 0.89136 to 0.90445, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 307ms/step - accuracy: 0.9245 - loss: 0.2198 - val_accuracy: 0.9045 - val_loss: 0.2512 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9417 - loss: 0.1910\nEpoch 9: val_accuracy improved from 0.90445 to 0.90838, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9417 - loss: 0.1909 - val_accuracy: 0.9084 - val_loss: 0.2412 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.9486 - loss: 0.1703\nEpoch 10: val_accuracy improved from 0.90838 to 0.91492, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 308ms/step - accuracy: 0.9486 - loss: 0.1702 - val_accuracy: 0.9149 - val_loss: 0.2260 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9568 - loss: 0.1509\nEpoch 11: val_accuracy improved from 0.91492 to 0.91885, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9568 - loss: 0.1508 - val_accuracy: 0.9188 - val_loss: 0.2227 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.9599 - loss: 0.1393\nEpoch 12: val_accuracy did not improve from 0.91885\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 289ms/step - accuracy: 0.9599 - loss: 0.1393 - val_accuracy: 0.9188 - val_loss: 0.2201 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.9643 - loss: 0.1312\nEpoch 13: val_accuracy improved from 0.91885 to 0.92670, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 310ms/step - accuracy: 0.9644 - loss: 0.1310 - val_accuracy: 0.9267 - val_loss: 0.2013 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9689 - loss: 0.1149\nEpoch 14: val_accuracy improved from 0.92670 to 0.93325, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9690 - loss: 0.1148 - val_accuracy: 0.9332 - val_loss: 0.1903 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9720 - loss: 0.1101\nEpoch 15: val_accuracy improved from 0.93325 to 0.93586, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9720 - loss: 0.1100 - val_accuracy: 0.9359 - val_loss: 0.1804 - learning_rate: 0.0010\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9763 - loss: 0.0918\nEpoch 16: val_accuracy improved from 0.93586 to 0.93848, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9763 - loss: 0.0919 - val_accuracy: 0.9385 - val_loss: 0.1779 - learning_rate: 0.0010\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 260ms/step - accuracy: 0.9748 - loss: 0.0935\nEpoch 17: val_accuracy did not improve from 0.93848\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 287ms/step - accuracy: 0.9749 - loss: 0.0934 - val_accuracy: 0.9372 - val_loss: 0.1656 - learning_rate: 0.0010\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9801 - loss: 0.0827\nEpoch 18: val_accuracy improved from 0.93848 to 0.93979, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 318ms/step - accuracy: 0.9801 - loss: 0.0828 - val_accuracy: 0.9398 - val_loss: 0.1601 - learning_rate: 0.0010\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9769 - loss: 0.0833\nEpoch 19: val_accuracy did not improve from 0.93979\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 288ms/step - accuracy: 0.9770 - loss: 0.0831 - val_accuracy: 0.9385 - val_loss: 0.1520 - learning_rate: 0.0010\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - accuracy: 0.9838 - loss: 0.0724\nEpoch 20: val_accuracy improved from 0.93979 to 0.94503, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 310ms/step - accuracy: 0.9838 - loss: 0.0724 - val_accuracy: 0.9450 - val_loss: 0.1413 - learning_rate: 0.0010\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:55:10.286749Z","iopub.execute_input":"2024-06-06T17:55:10.287649Z","iopub.status.idle":"2024-06-06T17:55:10.495352Z","shell.execute_reply.started":"2024-06-06T17:55:10.287613Z","shell.execute_reply":"2024-06-06T17:55:10.494408Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:55:10.496378Z","iopub.execute_input":"2024-06-06T17:55:10.496643Z","iopub.status.idle":"2024-06-06T17:55:10.703117Z","shell.execute_reply.started":"2024-06-06T17:55:10.496621Z","shell.execute_reply":"2024-06-06T17:55:10.702279Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:55:10.705634Z","iopub.execute_input":"2024-06-06T17:55:10.706237Z","iopub.status.idle":"2024-06-06T17:55:59.498338Z","shell.execute_reply.started":"2024-06-06T17:55:10.706201Z","shell.execute_reply":"2024-06-06T17:55:59.497371Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.1922\nTest Loss: 0.1494087427854538\nTest Accuracy: 0.95033860206604\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Xception","metadata":{}},{"cell_type":"code","source":"from keras.applications import Xception\nfrom keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\n# Loading model\nxception_model = Xception(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=xception_model.input, outputs=xception_model.get_layer('block14_sepconv2_act').output)\n\n# Freezing convolutional layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Adding dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(rate=0.5)(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# binding model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1)\n\nmodel.summary() # model summary","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:55:59.499488Z","iopub.execute_input":"2024-06-06T17:55:59.499780Z","iopub.status.idle":"2024-06-06T17:56:01.414485Z","shell.execute_reply.started":"2024-06-06T17:55:59.499756Z","shell.execute_reply":"2024-06-06T17:56:01.413601Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m83683744/83683744\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_11\"\u001b[0m\n","text/html":"
Model: \"functional_11\"\n
\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_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1_conv1_bn[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ block1_conv1_act… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1_conv2_bn[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m8,768\u001b[0m │ block1_conv2_act… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ block2_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m17,536\u001b[0m │ block2_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ block2_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ block1_conv2_act… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2_sepconv2_… │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m33,920\u001b[0m │ block3_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m67,840\u001b[0m │ block3_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,768\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3_sepconv2_… │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_1 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n│ │ \u001b[38;5;34m256\u001b[0m) │ │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m188,672\u001b[0m │ block4_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block4_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m186,368\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4_sepconv2_… │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_2 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5_sepconv2_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv3_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_3 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5_sepconv3_… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6_sepconv2_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv3_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_4 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6_sepconv3_… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7_sepconv2_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv3_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_5 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7_sepconv3_… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block8_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block8_sepconv2_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv3_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_6 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block8_sepconv3_… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv1_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block9_sepconv1_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv2_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block9_sepconv2_… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv3_… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_7 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block9_sepconv3_… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv1… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv1… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block10_sepconv1… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv2… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv2… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block10_sepconv2… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv3… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv3… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_8 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block10_sepconv3… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv1… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv1… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block11_sepconv1… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv2… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv2… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block11_sepconv2… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv3… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv3… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_9 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block11_sepconv3… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv1… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv1… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block12_sepconv1… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv2… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv2… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block12_sepconv2… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv3… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv3… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_10 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block12_sepconv3… │\n│ │ \u001b[38;5;34m728\u001b[0m) │ │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m536,536\u001b[0m │ block13_sepconv1… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m2,912\u001b[0m │ block13_sepconv1… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block13_sepconv1… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m728\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m752,024\u001b[0m │ block13_sepconv2… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ block13_sepconv2… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m745,472\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block13_sepconv2… │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_11 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block13_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m1024\u001b[0m) │ │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,582,080\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m1536\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block14_sepconv1… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1536\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block14_sepconv1… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1536\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m3,159,552\u001b[0m │ block14_sepconv1… │\n│ (\u001b[38;5;33mSeparableConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ block14_sepconv2… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block14_sepconv2… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block14_sepconv2… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m2,098,176\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_2       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1        │ (None, 111, 111,  │        864 │ input_layer_2[0]… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1_bn     │ (None, 111, 111,  │        128 │ block1_conv1[0][… │\n│ (BatchNormalizatio… │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv1_act    │ (None, 111, 111,  │          0 │ block1_conv1_bn[… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2        │ (None, 109, 109,  │     18,432 │ block1_conv1_act… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2_bn     │ (None, 109, 109,  │        256 │ block1_conv2[0][… │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1_conv2_act    │ (None, 109, 109,  │          0 │ block1_conv2_bn[… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv1     │ (None, 109, 109,  │      8,768 │ block1_conv2_act… │\n│ (SeparableConv2D)   │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv1_bn  │ (None, 109, 109,  │        512 │ block2_sepconv1[… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2_act │ (None, 109, 109,  │          0 │ block2_sepconv1_… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2     │ (None, 109, 109,  │     17,536 │ block2_sepconv2_… │\n│ (SeparableConv2D)   │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_sepconv2_bn  │ (None, 109, 109,  │        512 │ block2_sepconv2[… │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (Conv2D)     │ (None, 55, 55,    │      8,192 │ block1_conv2_act… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2_pool         │ (None, 55, 55,    │          0 │ block2_sepconv2_… │\n│ (MaxPooling2D)      │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalization │ (None, 55, 55,    │        512 │ conv2d[0][0]      │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add (Add)           │ (None, 55, 55,    │          0 │ block2_pool[0][0… │\n│                     │ 128)              │            │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1_act │ (None, 55, 55,    │          0 │ add[0][0]         │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1     │ (None, 55, 55,    │     33,920 │ block3_sepconv1_… │\n│ (SeparableConv2D)   │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv1_bn  │ (None, 55, 55,    │      1,024 │ block3_sepconv1[… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2_act │ (None, 55, 55,    │          0 │ block3_sepconv1_… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2     │ (None, 55, 55,    │     67,840 │ block3_sepconv2_… │\n│ (SeparableConv2D)   │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_sepconv2_bn  │ (None, 55, 55,    │      1,024 │ block3_sepconv2[… │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (Conv2D)   │ (None, 28, 28,    │     32,768 │ add[0][0]         │\n│                     │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3_pool         │ (None, 28, 28,    │          0 │ block3_sepconv2_… │\n│ (MaxPooling2D)      │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 28, 28,    │      1,024 │ conv2d_1[0][0]    │\n│ (BatchNormalizatio… │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_1 (Add)         │ (None, 28, 28,    │          0 │ block3_pool[0][0… │\n│                     │ 256)              │            │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1_act │ (None, 28, 28,    │          0 │ add_1[0][0]       │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1     │ (None, 28, 28,    │    188,672 │ block4_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv1_bn  │ (None, 28, 28,    │      2,912 │ block4_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2_act │ (None, 28, 28,    │          0 │ block4_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2     │ (None, 28, 28,    │    536,536 │ block4_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_sepconv2_bn  │ (None, 28, 28,    │      2,912 │ block4_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (Conv2D)   │ (None, 14, 14,    │    186,368 │ add_1[0][0]       │\n│                     │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4_pool         │ (None, 14, 14,    │          0 │ block4_sepconv2_… │\n│ (MaxPooling2D)      │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 14, 14,    │      2,912 │ conv2d_2[0][0]    │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_2 (Add)         │ (None, 14, 14,    │          0 │ block4_pool[0][0… │\n│                     │ 728)              │            │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1_act │ (None, 14, 14,    │          0 │ add_2[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1     │ (None, 14, 14,    │    536,536 │ block5_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv1_bn  │ (None, 14, 14,    │      2,912 │ block5_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2_act │ (None, 14, 14,    │          0 │ block5_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2     │ (None, 14, 14,    │    536,536 │ block5_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv2_bn  │ (None, 14, 14,    │      2,912 │ block5_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3_act │ (None, 14, 14,    │          0 │ block5_sepconv2_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3     │ (None, 14, 14,    │    536,536 │ block5_sepconv3_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5_sepconv3_bn  │ (None, 14, 14,    │      2,912 │ block5_sepconv3[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_3 (Add)         │ (None, 14, 14,    │          0 │ block5_sepconv3_… │\n│                     │ 728)              │            │ add_2[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1_act │ (None, 14, 14,    │          0 │ add_3[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1     │ (None, 14, 14,    │    536,536 │ block6_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv1_bn  │ (None, 14, 14,    │      2,912 │ block6_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2_act │ (None, 14, 14,    │          0 │ block6_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2     │ (None, 14, 14,    │    536,536 │ block6_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv2_bn  │ (None, 14, 14,    │      2,912 │ block6_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3_act │ (None, 14, 14,    │          0 │ block6_sepconv2_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3     │ (None, 14, 14,    │    536,536 │ block6_sepconv3_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6_sepconv3_bn  │ (None, 14, 14,    │      2,912 │ block6_sepconv3[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_4 (Add)         │ (None, 14, 14,    │          0 │ block6_sepconv3_… │\n│                     │ 728)              │            │ add_3[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1_act │ (None, 14, 14,    │          0 │ add_4[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1     │ (None, 14, 14,    │    536,536 │ block7_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv1_bn  │ (None, 14, 14,    │      2,912 │ block7_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2_act │ (None, 14, 14,    │          0 │ block7_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2     │ (None, 14, 14,    │    536,536 │ block7_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv2_bn  │ (None, 14, 14,    │      2,912 │ block7_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3_act │ (None, 14, 14,    │          0 │ block7_sepconv2_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3     │ (None, 14, 14,    │    536,536 │ block7_sepconv3_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7_sepconv3_bn  │ (None, 14, 14,    │      2,912 │ block7_sepconv3[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_5 (Add)         │ (None, 14, 14,    │          0 │ block7_sepconv3_… │\n│                     │ 728)              │            │ add_4[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1_act │ (None, 14, 14,    │          0 │ add_5[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1     │ (None, 14, 14,    │    536,536 │ block8_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv1_bn  │ (None, 14, 14,    │      2,912 │ block8_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2_act │ (None, 14, 14,    │          0 │ block8_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2     │ (None, 14, 14,    │    536,536 │ block8_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv2_bn  │ (None, 14, 14,    │      2,912 │ block8_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3_act │ (None, 14, 14,    │          0 │ block8_sepconv2_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3     │ (None, 14, 14,    │    536,536 │ block8_sepconv3_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block8_sepconv3_bn  │ (None, 14, 14,    │      2,912 │ block8_sepconv3[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_6 (Add)         │ (None, 14, 14,    │          0 │ block8_sepconv3_… │\n│                     │ 728)              │            │ add_5[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1_act │ (None, 14, 14,    │          0 │ add_6[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1     │ (None, 14, 14,    │    536,536 │ block9_sepconv1_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv1_bn  │ (None, 14, 14,    │      2,912 │ block9_sepconv1[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2_act │ (None, 14, 14,    │          0 │ block9_sepconv1_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2     │ (None, 14, 14,    │    536,536 │ block9_sepconv2_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv2_bn  │ (None, 14, 14,    │      2,912 │ block9_sepconv2[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3_act │ (None, 14, 14,    │          0 │ block9_sepconv2_… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3     │ (None, 14, 14,    │    536,536 │ block9_sepconv3_… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block9_sepconv3_bn  │ (None, 14, 14,    │      2,912 │ block9_sepconv3[… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_7 (Add)         │ (None, 14, 14,    │          0 │ block9_sepconv3_… │\n│                     │ 728)              │            │ add_6[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1_a… │ (None, 14, 14,    │          0 │ add_7[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1    │ (None, 14, 14,    │    536,536 │ block10_sepconv1… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv1_bn │ (None, 14, 14,    │      2,912 │ block10_sepconv1… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2_a… │ (None, 14, 14,    │          0 │ block10_sepconv1… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2    │ (None, 14, 14,    │    536,536 │ block10_sepconv2… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv2_bn │ (None, 14, 14,    │      2,912 │ block10_sepconv2… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3_a… │ (None, 14, 14,    │          0 │ block10_sepconv2… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3    │ (None, 14, 14,    │    536,536 │ block10_sepconv3… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block10_sepconv3_bn │ (None, 14, 14,    │      2,912 │ block10_sepconv3… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_8 (Add)         │ (None, 14, 14,    │          0 │ block10_sepconv3… │\n│                     │ 728)              │            │ add_7[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1_a… │ (None, 14, 14,    │          0 │ add_8[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1    │ (None, 14, 14,    │    536,536 │ block11_sepconv1… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv1_bn │ (None, 14, 14,    │      2,912 │ block11_sepconv1… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2_a… │ (None, 14, 14,    │          0 │ block11_sepconv1… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2    │ (None, 14, 14,    │    536,536 │ block11_sepconv2… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv2_bn │ (None, 14, 14,    │      2,912 │ block11_sepconv2… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3_a… │ (None, 14, 14,    │          0 │ block11_sepconv2… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3    │ (None, 14, 14,    │    536,536 │ block11_sepconv3… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block11_sepconv3_bn │ (None, 14, 14,    │      2,912 │ block11_sepconv3… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_9 (Add)         │ (None, 14, 14,    │          0 │ block11_sepconv3… │\n│                     │ 728)              │            │ add_8[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1_a… │ (None, 14, 14,    │          0 │ add_9[0][0]       │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1    │ (None, 14, 14,    │    536,536 │ block12_sepconv1… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv1_bn │ (None, 14, 14,    │      2,912 │ block12_sepconv1… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2_a… │ (None, 14, 14,    │          0 │ block12_sepconv1… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2    │ (None, 14, 14,    │    536,536 │ block12_sepconv2… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv2_bn │ (None, 14, 14,    │      2,912 │ block12_sepconv2… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3_a… │ (None, 14, 14,    │          0 │ block12_sepconv2… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3    │ (None, 14, 14,    │    536,536 │ block12_sepconv3… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block12_sepconv3_bn │ (None, 14, 14,    │      2,912 │ block12_sepconv3… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_10 (Add)        │ (None, 14, 14,    │          0 │ block12_sepconv3… │\n│                     │ 728)              │            │ add_9[0][0]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1_a… │ (None, 14, 14,    │          0 │ add_10[0][0]      │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1    │ (None, 14, 14,    │    536,536 │ block13_sepconv1… │\n│ (SeparableConv2D)   │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv1_bn │ (None, 14, 14,    │      2,912 │ block13_sepconv1… │\n│ (BatchNormalizatio… │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2_a… │ (None, 14, 14,    │          0 │ block13_sepconv1… │\n│ (Activation)        │ 728)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2    │ (None, 14, 14,    │    752,024 │ block13_sepconv2… │\n│ (SeparableConv2D)   │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_sepconv2_bn │ (None, 14, 14,    │      4,096 │ block13_sepconv2… │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (Conv2D)   │ (None, 7, 7,      │    745,472 │ add_10[0][0]      │\n│                     │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block13_pool        │ (None, 7, 7,      │          0 │ block13_sepconv2… │\n│ (MaxPooling2D)      │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 7, 7,      │      4,096 │ conv2d_3[0][0]    │\n│ (BatchNormalizatio… │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ add_11 (Add)        │ (None, 7, 7,      │          0 │ block13_pool[0][… │\n│                     │ 1024)             │            │ batch_normalizat… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1    │ (None, 7, 7,      │  1,582,080 │ add_11[0][0]      │\n│ (SeparableConv2D)   │ 1536)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1_bn │ (None, 7, 7,      │      6,144 │ block14_sepconv1… │\n│ (BatchNormalizatio… │ 1536)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv1_a… │ (None, 7, 7,      │          0 │ block14_sepconv1… │\n│ (Activation)        │ 1536)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2    │ (None, 7, 7,      │  3,159,552 │ block14_sepconv1… │\n│ (SeparableConv2D)   │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2_bn │ (None, 7, 7,      │      8,192 │ block14_sepconv2… │\n│ (BatchNormalizatio… │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block14_sepconv2_a… │ (None, 7, 7,      │          0 │ block14_sepconv2… │\n│ (Activation)        │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 2048)      │          0 │ block14_sepconv2… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_4 (Dense)     │ (None, 1024)      │  2,098,176 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_2 (Dropout) │ (None, 1024)      │          0 │ dense_4[0][0]     │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_5 (Dense)     │ (None, 1)         │      1,025 │ dropout_2[0][0]   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m22,960,681\u001b[0m (87.59 MB)\n","text/html":"
 Total params: 22,960,681 (87.59 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,099,201\u001b[0m (8.01 MB)\n","text/html":"
 Trainable params: 2,099,201 (8.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20,861,480\u001b[0m (79.58 MB)\n","text/html":"
 Non-trainable params: 20,861,480 (79.58 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T17:56:01.415619Z","iopub.execute_input":"2024-06-06T17:56:01.415890Z","iopub.status.idle":"2024-06-06T18:02:51.864779Z","shell.execute_reply.started":"2024-06-06T17:56:01.415867Z","shell.execute_reply":"2024-06-06T18:02:51.863748Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Epoch 1/20\n","output_type":"stream"},{"name":"stderr","text":"2024-06-06 17:56:30.047520: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng28{k2=3,k3=0} for conv (f16[250,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:30.234907: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.187502233s\nTrying algorithm eng28{k2=3,k3=0} for conv (f16[250,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:32.949453: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng28{k2=3,k3=0} for conv (f16[250,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:33.136750: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.187393335s\nTrying algorithm eng28{k2=3,k3=0} for conv (f16[250,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:41.081975: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng4{} for conv (f16[250,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:41.906450: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.824666024s\nTrying algorithm eng4{} for conv (f16[250,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:44.135229: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng4{} for conv (f16[250,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:56:44.962791: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.827741692s\nTrying algorithm eng4{} for conv (f16[250,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\nW0000 00:00:1717696645.915664 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m36/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.6315 - loss: 0.8962","output_type":"stream"},{"name":"stderr","text":"2024-06-06 17:57:44.059104: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng28{k2=3,k3=0} for conv (f16[165,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:57:44.107357: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.048432425s\nTrying algorithm eng28{k2=3,k3=0} for conv (f16[165,109,109,64]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,64]{3,2,1,0}, f16[64,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=64, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:57:51.755734: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng4{} for conv (f16[165,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:57:51.975383: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.219823771s\nTrying algorithm eng4{} for conv (f16[165,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:57:53.811387: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng4{} for conv (f16[165,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 17:57:54.030997: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.21973891s\nTrying algorithm eng4{} for conv (f16[165,109,109,128]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,109,109,128]{3,2,1,0}, f16[128,3,3,1]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, feature_group_count=128, custom_call_target=\"__cudnn$convForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.6339 - loss: 0.8890 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717696703.616907 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717696708.300280 110 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.85995, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 2s/step - accuracy: 0.6361 - loss: 0.8822 - val_accuracy: 0.8599 - val_loss: 0.3705 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - accuracy: 0.8693 - loss: 0.3136\nEpoch 2: val_accuracy improved from 0.85995 to 0.91230, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 334ms/step - accuracy: 0.8700 - loss: 0.3125 - val_accuracy: 0.9123 - val_loss: 0.2280 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.9520 - loss: 0.1653\nEpoch 3: val_accuracy improved from 0.91230 to 0.93979, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 326ms/step - accuracy: 0.9522 - loss: 0.1648 - val_accuracy: 0.9398 - val_loss: 0.1775 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - accuracy: 0.9730 - loss: 0.1031\nEpoch 4: val_accuracy improved from 0.93979 to 0.95157, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 331ms/step - accuracy: 0.9731 - loss: 0.1028 - val_accuracy: 0.9516 - val_loss: 0.1423 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 281ms/step - accuracy: 0.9825 - loss: 0.0702\nEpoch 5: val_accuracy improved from 0.95157 to 0.96204, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 329ms/step - accuracy: 0.9826 - loss: 0.0700 - val_accuracy: 0.9620 - val_loss: 0.1100 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.9916 - loss: 0.0468\nEpoch 6: val_accuracy improved from 0.96204 to 0.96859, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 325ms/step - accuracy: 0.9916 - loss: 0.0468 - val_accuracy: 0.9686 - val_loss: 0.0895 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.9929 - loss: 0.0362\nEpoch 7: val_accuracy improved from 0.96859 to 0.97120, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 322ms/step - accuracy: 0.9929 - loss: 0.0362 - val_accuracy: 0.9712 - val_loss: 0.0871 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - accuracy: 0.9925 - loss: 0.0331\nEpoch 8: val_accuracy improved from 0.97120 to 0.97513, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 322ms/step - accuracy: 0.9925 - loss: 0.0331 - val_accuracy: 0.9751 - val_loss: 0.0802 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.9967 - loss: 0.0208\nEpoch 9: val_accuracy improved from 0.97513 to 0.98298, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 323ms/step - accuracy: 0.9967 - loss: 0.0208 - val_accuracy: 0.9830 - val_loss: 0.0711 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.9976 - loss: 0.0171\nEpoch 10: val_accuracy improved from 0.98298 to 0.98560, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 326ms/step - accuracy: 0.9976 - loss: 0.0170 - val_accuracy: 0.9856 - val_loss: 0.0585 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.9986 - loss: 0.0131\nEpoch 11: val_accuracy did not improve from 0.98560\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 303ms/step - accuracy: 0.9986 - loss: 0.0131 - val_accuracy: 0.9843 - val_loss: 0.0597 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 281ms/step - accuracy: 0.9988 - loss: 0.0125\nEpoch 12: val_accuracy improved from 0.98560 to 0.98822, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 330ms/step - accuracy: 0.9988 - loss: 0.0124 - val_accuracy: 0.9882 - val_loss: 0.0541 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.9992 - loss: 0.0097\nEpoch 13: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 304ms/step - accuracy: 0.9992 - loss: 0.0097 - val_accuracy: 0.9856 - val_loss: 0.0711 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.9996 - loss: 0.0079\nEpoch 14: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9996 - loss: 0.0078 - val_accuracy: 0.9882 - val_loss: 0.0545 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 0.9996 - loss: 0.0065\nEpoch 15: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 304ms/step - accuracy: 0.9996 - loss: 0.0065 - val_accuracy: 0.9882 - val_loss: 0.0656 - learning_rate: 0.0010\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.9999 - loss: 0.0057\nEpoch 16: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.9999 - loss: 0.0057 - val_accuracy: 0.9882 - val_loss: 0.0458 - learning_rate: 0.0010\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 281ms/step - accuracy: 0.9993 - loss: 0.0052\nEpoch 17: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 310ms/step - accuracy: 0.9993 - loss: 0.0052 - val_accuracy: 0.9882 - val_loss: 0.0653 - learning_rate: 0.0010\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 279ms/step - accuracy: 0.9999 - loss: 0.0051\nEpoch 18: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 307ms/step - accuracy: 0.9999 - loss: 0.0051 - val_accuracy: 0.9804 - val_loss: 0.0778 - learning_rate: 0.0010\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.9993 - loss: 0.0043\nEpoch 19: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 306ms/step - accuracy: 0.9993 - loss: 0.0043 - val_accuracy: 0.9882 - val_loss: 0.0633 - learning_rate: 0.0010\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - accuracy: 1.0000 - loss: 0.0034\nEpoch 20: val_accuracy did not improve from 0.98822\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 304ms/step - accuracy: 1.0000 - loss: 0.0034 - val_accuracy: 0.9869 - val_loss: 0.0706 - learning_rate: 0.0010\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:02:51.866535Z","iopub.execute_input":"2024-06-06T18:02:51.866835Z","iopub.status.idle":"2024-06-06T18:02:52.141433Z","shell.execute_reply.started":"2024-06-06T18:02:51.866810Z","shell.execute_reply":"2024-06-06T18:02:52.140535Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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0FBQQQEBLi2DRgwgNzcXKqqqggODq6zj81mw2azebbyjYgMCSI6NIjC8mr2HimnX2Kkz84tIiIifmy5CQ4OJiMjg+XLl7u2ORwOli9fzvDhw+vd58wzz2Tbtm04HA7Xti1bttC1a9d6g42/HJ0xpUHFIiIivubXbqkZM2bw7LPP8uKLL7Jp0yZuuOEGSktLXbOnpkyZwsyZM13lb7jhBg4fPswtt9zCli1beO+993jggQeYPn26vy6hXpoOLiIi4j9+nQo+efJkDh48yN13301ubi7p6eksWbLENch4165dWK1H81dycjIffvghf/zjHxk8eDBJSUnccsst3H777f66hHolxZgzptRyIyIi4nsWwzAMf1fCl4qKioiOjqawsJCoqCivnGP+lzu4790fueCURP59ZYZXziEiItKRNOf3d5uaLdVWqFtKRETEfxRuvMC5kJ/CjYiIiO8p3HhBUm3LzeHSKsqqtNaNiIiILynceEF0aBCRIeZY7b1qvREREfEphRsvUdeUiIiIfyjceInuDi4iIuIfCjde4poxpbuDi4iI+JTCjZdoOriIiIh/KNx4icbciIiI+IfCjZc4W240W0pERMS3FG68xBlu8ksqqai2+7k2IiIiHYfCjZdEhwYRYTPXulHXlIiIiO8o3HiJxWJxTQffqxlTIiIiPqNw40VHZ0xprRsRERFfUbjxIk0HFxER8T2FGy/SdHARERHfU7jxoiTXdHB1S4mIiPiKwo0XqVtKRETE9xRuvMjZLXWgWGvdiIiI+IrCjRfFhgURGhQAwP7CCj/XRkREpGNQuPEii8Wi6eAiIiI+pnDjZRp3IyIi4lsKN152dDq4Wm5ERER8QeHGy5J0d3ARERGfUrjxMnVLiYiI+JbCjZdplWIRERHfUrjxMuedwfOKK6iqcfi5NiIiIu2fwo2XxUUEYwu0Yhiwv1CtNyIiIt6mcONl7mvdKNyIiIh4m8KND2g6uIiIiO8o3PiApoOLiIj4jsKND6hbSkRExHcUbnxA08FFRER8R+HGB5zTwfcWKNyIiIh4m8KNDyTXdkvtLyyn2q61bkRERLxJ4cYH4iJsBAdacRiQW1jh7+qIiIi0awo3PmC1Wugeo0HFIiIivqBw4yNJrhlTWutGRETEmxRufETTwUVERHxD4cZHNB1cRETENxRufOTodHB1S4mIiHiTwo2PqFtKRETEN1pFuJk7dy6pqamEhISQmZnJypUrGyy7YMECLBaL2yMkJMSHtW0ZZ7fU/sIKarTWjYiIiNf4PdwsXLiQGTNmMGvWLFavXk1aWhpjxozhwIEDDe4TFRXF/v37XY+cnBwf1rhl4iNtBAVYsDsM8oor/V0dERGRdsvv4ebxxx/n2muvZdq0aQwcOJB58+YRFhbG/PnzG9zHYrGQmJjoeiQkJPiwxi1jtVro5lzr5rDG3YiIiHiLX8NNVVUVq1atIisry7XNarWSlZXFihUrGtyvpKSElJQUkpOTmThxIhs3bmywbGVlJUVFRW4Pf9G4GxEREe/za7jJz8/HbrfXaXlJSEggNze33n369evH/Pnzeeedd3jppZdwOBycccYZ7Nmzp97y2dnZREdHux7Jyckev46m6h6j6eAiIiLe5vduqeYaPnw4U6ZMIT09nREjRvDmm2/SpUsXnn766XrLz5w5k8LCQtdj9+7dPq7xUc5VijUdXERExHsC/XnyuLg4AgICyMvLc9uel5dHYmJik44RFBTEkCFD2LZtW73v22w2bDbbCdfVE9QtJSIi4n1+bbkJDg4mIyOD5cuXu7Y5HA6WL1/O8OHDm3QMu93O+vXr6dq1q7eq6TFapVhERMT7/NpyAzBjxgymTp3K0KFDGTZsGLNnz6a0tJRp06YBMGXKFJKSksjOzgbgvvvu4/TTT6dPnz4UFBTwyCOPkJOTwzXXXOPPy2gSZ8vN/sJy7A6DAKvFzzUSERFpf/webiZPnszBgwe5++67yc3NJT09nSVLlrgGGe/atQur9WgD05EjR7j22mvJzc0lNjaWjIwMvv76awYOHOivS2iyhKgQAq0Wqu0GB4or6Bod6u8qiYiItDsWwzAMf1fCl4qKioiOjqawsJCoqCifn//shz9m9+FyFv1hOKeldvL5+UVERNqi5vz+bnOzpdo653TwvRp3IyIi4hUKNz6W5JoxpengIiIi3qBw42OaDi4iIuJdCjc+pungIiIi3qVw42PdXasUK9yIiIh4g8KNjyXV3hl875FyHI4ONVFNRETEJxRufKxrdAgBVgtVdgcHSyr9XR0REZF2R+HGxwIDrCRGhQAadyMiIuINCjd+oOngIiIi3qNw4weaDi4iIuI9Cjd+oOngIiIi3qNw4weaDi4iIuI9Cjd+0D1GY25ERES8ReHGD5zdUnuPlNPBbsouIiLidQo3fpAYHYLVApU1DvJLqvxdHRERkXZF4cYPggOtJLjWulHXlIiIiCcp3PiJpoOLiIh4h8KNn7jG3WjGlIiIiEcp3PhJd61SLCIi4hUKN36SFKNuKREREW9QuPETrVIsIiLiHQo3fuJapVhr3YiIiHiUwo2fdI0xp4KXV9s5XKq1bkRERDxF4cZPbIEBJETZAHVNiYiIeJLCjR9pOriIiIjnKdz4kaaDi4iIeJ7CjR9pOriIiIjnKdz4kaaDi4iIeJ7CjR8dOx1cREREPEPhxo+Sjhlzo7VuREREPEPhxo+cY25Kq+wUlFX7uTYiIiLtg8KNH4UEBdAl0lzrRtPBRUREPEPhxs80HVxERMSzFG78TNPBRUREPEvhxs80HVxERMSzFG787Gi3lMKNiIiIJyjc+FmSxtyIiIh4lMKNnyVrIT8RERGPUrjxs6QYc8xNcWUNheVa60ZEROREKdz4WWhwAHERwYC6pkRERDxB4aYV0HRwERERz1G4aQWc08E17kZEROTEKdy0ApoOLiIi4jmtItzMnTuX1NRUQkJCyMzMZOXKlU3a77XXXsNisTBp0iTvVtDLNB1cRETEc/webhYuXMiMGTOYNWsWq1evJi0tjTFjxnDgwIFG99u5cye33XYbZ599to9q6j1quREREfEcv4ebxx9/nGuvvZZp06YxcOBA5s2bR1hYGPPnz29wH7vdzpVXXsm9995Lr169fFhb73CNudGdwUVERE6YX8NNVVUVq1atIisry7XNarWSlZXFihUrGtzvvvvuIz4+nt///vfHPUdlZSVFRUVuj9bGOVuqsLyaogqtdSMiInIi/Bpu8vPzsdvtJCQkuG1PSEggNze33n2+/PJLnn/+eZ599tkmnSM7O5vo6GjXIzk5+YTr7WnhtkBiw4IAzZgSERE5UX7vlmqO4uJirrrqKp599lni4uKatM/MmTMpLCx0PXbv3u3lWraMpoOLiIh4RqA/Tx4XF0dAQAB5eXlu2/Py8khMTKxTfvv27ezcuZNf//rXrm0OhwOAwMBANm/eTO/evd32sdls2Gw2L9Tes7rHhrJ+b6FmTImIiJwgv7bcBAcHk5GRwfLly13bHA4Hy5cvZ/jw4XXK9+/fn/Xr17N27VrXY8KECZx33nmsXbu2VXY5NZVWKRYREfEMv7bcAMyYMYOpU6cydOhQhg0bxuzZsyktLWXatGkATJkyhaSkJLKzswkJCeHkk0922z8mJgagzva2xjkdXDOmRERETozfw83kyZM5ePAgd999N7m5uaSnp7NkyRLXIONdu3ZhtbapoUEt4hxzo5YbERGRE2MxDMPwdyV8qaioiOjoaAoLC4mKivJ3dVx+yi1i7OwviA0LYs3do/1dHRERkValOb+/23+TSBvhHHNzpKya0soaP9dGRESk7VK4aSUiQ4KIDq1d60bjbkRERFpM4aYV6a4baIqIiJwwhZtWRNPBRURETpzCTSuiVYpFREROnMJNK3K0W0rhRkREpKUUbloRjbkRERE5cQo3nlReAAe3tHj3JLXciIiInDCFG0/56T14pDcsvqnFh3COuTlUWkV5ld1TNRMREelQFG48pdsQcNTA7pVQcqBFh4gODSIyxLwjxt4CdU2JiIi0hMKNp0R1MwMOBmz+oMWHcU4H362uKRERkRZRuPGk/uPNn5vfb/EhNB1cRETkxCjceFK/2nCz/ROoLGnRITQdXERE5MQo3HhS/ACI7Qn2Stj+cYsOoengIiIiJ0bhxpMslqNdUz+916JDqOVGRETkxCjceFq/C8yfW5aAvabZu7vG3OjO4CIiIi2icONpyZkQ1hkqCmDX183e3dlyc7C4kopqrXUjIiLSXAo3nhYQCCeNNZ//1PxZU9GhQYQHBwBqvREREWkJhRtvcE0Jfw8Mo1m7WiwWTQcXERE5AQo33tDrPAgMhYJdkLeh2btrULGIiEjLKdx4Q3AY9D7ffN6CrilNBxcREWk5hRtv6V87a+qnd5u9q/Pu4BpzIyIi0nwKN95y0liwWCH3ByjY3axdnWNu1C0lIiLSfAo33hIeB8mnm8+bea8pdUuJiIi0nMKNN7m6ppq3WrHzzuB5RZVU1mitGxERkeZQuPEm52rFOV9B+ZEm79YpPJjQIHOtm/0FFd6omYiISLvVonCze/du9uzZ43q9cuVKbr31Vp555hmPVaxd6NwbugwARw1sXdrk3cy1bjQdXEREpCVaFG6uuOIKPvnkEwByc3MZNWoUK1eu5M477+S+++7zaAXbvBbeSFPjbkRERFqmReFmw4YNDBs2DIDXX3+dk08+ma+//pqXX36ZBQsWeLJ+bZ9z3M22ZVBT2eTdNB1cRESkZVoUbqqrq7HZbAAsW7aMCRMmANC/f3/279/vudq1B12HQGRXqCqBHZ83eTdNBxcREWmZFoWbQYMGMW/ePL744guWLl3K2LHmjSL37dtH586dPVrBNs9qPTqwuBkL+qlbSkREpGVaFG4eeughnn76ac4991wuv/xy0tLSAFi8eLGru0qO4eya2vwBOBxN2sU5HVwtNyIiIs0T2JKdzj33XPLz8ykqKiI2Nta1/brrriMsLMxjlWs3Us8BWxSU5MG+1dB96HF3cXZL5RVVUFXjIDhQs/ZFRESaokW/McvLy6msrHQFm5ycHGbPns3mzZuJj4/3aAXbhcBg6JNlPm9i11RcRDC2QCsOA3ILtdaNiIhIU7Uo3EycOJH//Oc/ABQUFJCZmcljjz3GpEmTeOqppzxawXbDNSW8abdisFgsrhlTGncjIiLSdC0KN6tXr+bss88G4I033iAhIYGcnBz+85//8K9//cujFWw3+o4CaxDkb4b8bU3axTVjStPBRUREmqxF4aasrIzIyEgAPvroIy666CKsViunn346OTk5Hq1guxESDalnmc83N21BP61SLCIi0nwtCjd9+vTh7bffZvfu3Xz44YeMHj0agAMHDhAVFeXRCrYrzVytWNPBRUREmq9F4ebuu+/mtttuIzU1lWHDhjF8+HDAbMUZMmSIRyvYrjjXu9m9EkoOHLe4czr4XrXciIiINFmLws0ll1zCrl27+P777/nwww9d20eOHMkTTzzhscq1O9FJ0G0IYJhr3hyHVikWERFpvhYvnpKYmMiQIUPYt2+f6w7hw4YNo3///h6rXLvUr7ZravPxZ00l13ZL5RZVUGNv2uJ/IiIiHV2Lwo3D4eC+++4jOjqalJQUUlJSiImJ4f7778fRxBV4OyznasXbP4HKkkaLxkXYCA6wYncY7NdaNyIiIk3SonBz5513MmfOHB588EHWrFnDmjVreOCBB3jyySe56667PF3H9iV+IMSmgr0Stn/caFGr1aK7g4uIiDRTi8LNiy++yHPPPccNN9zA4MGDGTx4MDfeeCPPPvssCxYsaPbx5s6dS2pqKiEhIWRmZrJy5coGy7755psMHTqUmJgYwsPDSU9P57///W9LLsM/LJZmdU1pOriIiEjztCjcHD58uN6xNf379+fw4cPNOtbChQuZMWMGs2bNYvXq1aSlpTFmzBgOHKh/NlGnTp248847WbFiBT/88APTpk1j2rRpbgObWz3nlPAtS8Be02hRTQcXERFpnhaFm7S0NObMmVNn+5w5cxg8eHCzjvX4449z7bXXMm3aNAYOHMi8efMICwtj/vz59ZY/99xzufDCCxkwYAC9e/fmlltuYfDgwXz55ZctuRT/SM6E0E5QfgR2rWi0qKaDi4iINE+L7gr+8MMPM378eJYtW+Za42bFihXs3r2b999v2r2TAKqqqli1ahUzZ850bbNarWRlZbFiReO/9AEMw+Djjz9m8+bNPPTQQ/WWqayspLKy0vW6qKioyfXzmoBA6DcO1r5sLujX8+wGi2o6uIiISPO0qOVmxIgRbNmyhQsvvJCCggIKCgq46KKL2LhxY7PGv+Tn52O320lISHDbnpCQQG5uboP7FRYWEhERQXBwMOPHj+fJJ59k1KhR9ZbNzs4mOjra9UhOTm5y/bzKuaDf5vfAMBos5uqWKlC3lIiISFO0qOUGoFu3bvzjH/9w27Zu3Tqef/55nnnmmROuWGMiIyNZu3YtJSUlLF++nBkzZtCrVy/OPffcOmVnzpzJjBkzXK+LiopaR8DpfR4EhkDBLsjbCIkn11vMOVtqf0EFdodBgNXiy1qKiIi0OS0ON54QFxdHQEAAeXl5btvz8vJITExscD+r1UqfPn0ASE9PZ9OmTWRnZ9cbbmw2GzabzaP19ojgcOh9vjlj6qf3Ggw38ZEhBAVYqLYb5BVV0K12DI6IiIjUr8UrFHtCcHAwGRkZLF++3LXN4XCwfPly11iepnA4HG7jatqMY7umGhBgtbgCjcbdiIiIHJ9fW24AZsyYwdSpUxk6dCjDhg1j9uzZlJaWMm3aNACmTJlCUlIS2dnZgDmGZujQofTu3ZvKykref/99/vvf//LUU0/58zJapt84sFhh/zoo2A0x9XeXdY8NJedQGXuOlDGsZycfV1JERKRtaVa4ueiiixp9v6CgoNkVmDx5MgcPHuTuu+8mNzeX9PR0lixZ4hpkvGvXLqzWow1MpaWl3HjjjezZs4fQ0FD69+/PSy+9xOTJk5t9br8LjzOnhe9aYd5IM/O6eotpOriIiEjTWQyjkak6v+BsTTmeF154ocUV8raioiKio6MpLCwkKirK39WBr5+Ej/4GPUfA1MX1FvnX8q08vnQLk4cm89AlzVtHSEREpD1ozu/vZrXctObQ0mb1u8AMNzlfQXkBhMbUKaLp4CIiIk3n1wHFAnTuDV36g6MGti6tt4i6pURERJpO4aY1cN5r6qd36327eydzleK9BeU4HE3uRRQREemQFG5aA+ddwrctg5q6U9oTIm0EWs21bg4Ut8Ep7yIiIj6kcNMadBsCkV2hqgR2fF7n7cAAK11jQgDdHVxEROR4FG5aA6vVXPMGzNWK6+Ead1OgcTciIiKNUbhpLZxdU5vfB4ejztu6O7iIiEjTKNy0Fj3PhuBIKMmDfavrvN2jdlDxj/uLfF0zERGRNkXhprUItEHfLPN5PV1T55zUBYCPNx2gtLLGlzUTERFpUxRuWpP+vzJ/1hNu0rpHk9I5jPJqO8s25dV5X0REREwKN61JnyywBkL+Zsjf5vaWxWJhYlo3ABav3eeP2omIiLQJCjetSWgMpJ5tPt9ct/VmQroZbj7bcpAjpVU+rJiIiEjboXDT2rhWK36/zlt94iMZ2DWKGofB+xv2+7hiIiIibYPCTWvjXO9m97dQcqDO287WG3VNiYiI1E/hprWJ7g5d0wEDtiyp8/ava8fdrNx5mP2FWvNGRETklxRuWqNGuqaSYkIZltoJw4B316lrSkRE5JcUblojZ7j5+ROoKq3z9q9ru6beWbfXl7USERFpExRuWqP4gRCTAjUVsP3jOm+PP6UrgVYLG/YWsf1giR8qKCIi0nop3LRGFkujC/p1Cg/m7L5xgAYWi4iI/JLCTWvV/wLz55YlYK97uwXXrKl1+zAMw5c1ExERadUUblqr5NMhNBbKj8CuFXXeHjUwkZAgKzvyS9mwVzfTFBERcVK4aa0CAuGk2jVvNtedNRVhC2TkgAQA3lmrgcUiIiJOCjetmbNr6qd3oZ6uJ+e9pv73wz7sDnVNiYiIgMJN69b7fAgMgYJdkLexztsj+nUhKiSQvKJKVu447IcKioiItD4KN61ZcDj0Os98Xk/XlC0wgHEndwVgsda8ERERARRuWj/XasXv1vv2xNpZU++vz6WqxuGrWomIiLRaCjet3UljAQvsXweFe+q8ndmrM/GRNgrLq/l8y0Hf109ERKSVUbhp7SK6QHKm+byee00FWC38arDzdgxa0E9EREThpi1wdk1trrtaMRztmlr2Yx6llXUX/BMREelIFG7aAme42fkllBfUeXtw92hSOodRXm1n2aY839ZNRESklVG4aQs694Yu/cFRA1s/qvO2xWJxrXnzju41JSIiHZzCTVvhvJHmsnugqG6Acd5r6vMtBzlSWuXDiomIiLQuCjdtxRk3QdxJULQXXr4UKtzvJ9UnPpKBXaOocRi8v2G/nyopIiLifwo3bUVoLFy5CMLjIW89LJoK9mq3Is6BxYvVNSUiIh2Ywk1bEpsKVyyEoDDY/jG8e6vbPad+VTvuZuXOw+wrKPdPHUVERPxM4aatSToVLnkBLFZY8xJ8/sjRt2JCGZbaCcOAd39Q642IiHRMCjdtUb+xMP4x8/kn/4C1r7jecg4sXqwF/UREpINSuGmrhv4Ozvqj+XzxzbD9EwAuOKUrgVYLG/YWsf1giR8rKCIi4h8KN23Z+XfDyZeY698svApyN9ApPJiz+8YBGlgsIiIdk8JNW2a1wqR/Q8pZUFUML/8GCve6dU0Zxww4FhER6QgUbtq6QBtc9hLE9YPiffDKpYzqHUZIkJUd+aWs31vo7xqKiIj4lMJNexAaC799AyISIG8DEW9PY3T/zoC6pkREpONpFeFm7ty5pKamEhISQmZmJitXrmyw7LPPPsvZZ59NbGwssbGxZGVlNVq+w4jpUbsGTjj8/Cm3Vz8FGPzvh33YHeqaEhGRjsPv4WbhwoXMmDGDWbNmsXr1atLS0hgzZgwHDhyot/ynn37K5ZdfzieffMKKFStITk5m9OjR7N2718c1b4W6DYHfLACLlaSdb/KXkLfJK6pk5Y7D/q6ZiIiIz1gMP484zczM5LTTTmPOnDkAOBwOkpOTufnmm7njjjuOu7/dbic2NpY5c+YwZcqU45YvKioiOjqawsJCoqKiTrj+rdL3L5irFwO3VV9PUMZvyb5osH/rJCIicgKa8/vbry03VVVVrFq1iqysLNc2q9VKVlYWK1asaNIxysrKqK6uplOnTvW+X1lZSVFRkduj3Rs6Dc7+EwDZgc9x5IcPqapx+LlSIiIivuHXcJOfn4/dbichIcFte0JCArm5uU06xu233063bt3cAtKxsrOziY6Odj2Sk5NPuN5twvl34Tj5NwRZ7DxiPMbqlZ/7u0YiIiI+4fcxNyfiwQcf5LXXXuOtt94iJCSk3jIzZ86ksLDQ9di9e7ePa+knFgvWSXPZGXEqkZZy+n38eyjUuCQREWn//Bpu4uLiCAgIIC8vz217Xl4eiYmJje776KOP8uCDD/LRRx8xeHDD40lsNhtRUVFujw4j0EbJpAVscSQRW5OP46VLoELr3oiISPvm13ATHBxMRkYGy5cvd21zOBwsX76c4cOHN7jfww8/zP3338+SJUsYOnSoL6raZg3q3YO7wmdxwIjBevBH8zYNNVX+rpaIiIjX+L1basaMGTz77LO8+OKLbNq0iRtuuIHS0lKmTZsGwJQpU5g5c6ar/EMPPcRdd93F/PnzSU1NJTc3l9zcXEpKdJPI+lgsFjKHpDOt6i9UWEJgx2fwv/8D3ZZBRETaKb+Hm8mTJ/Poo49y9913k56eztq1a1myZIlrkPGuXbvYv3+/q/xTTz1FVVUVl1xyCV27dnU9Hn30UX9dQqs3Ib0bG41Ubqy6BcMSAOtehU+z/V0tERERr/D7Oje+1iHWuanHBf/8gh/3F/Ha0J84fcN95sYJc+DUq/xbMRERkSZoM+vciO9MrL1T+BOHzoCzbzM3/u8W2LbMj7USERHxPIWbDuLXaWa4WbnzMPtO/RMMvgwMO7w+Ffb/4OfaiYiIeI7CTQfRLSaUYamdMAx4d/1+mPAkpJ4NVSXw8m+goIOs/yMiIu2ewk0HMqG2a2rxun0QGAyTX4IuA6Ak1ww45QX+raCIiIgHKNx0IBec0pVAq4UNe4vYfrAEQmPgykUQkQgHN8HC30J1ub+rKSIickIUbjqQTuHBnN03DoDFa/eZG2OSzYATHAE7v4D5Y9VFJSIibZrCTQczMT0JMLumXKsAdB0MV7wOoZ1g/1p4ZgTs0I02RUSkbVK46WBGDUwgJMjKjvxS1u895j5TqWfC9Z9B4mAoOwT/mQQr5molYxERaXMUbjqYcFsgWQPM1Z9dXVNOMT3g9x8dnSb+4V/hzWuhqswPNRUREWkZhZsOyNk19b8f9mF3/KJlJigULpwH4x4GSwCsXwTPj4YjO31fURERkRZQuOmAzjkpjqiQQPKKKlm543DdAhYLZF4PUxdDeBfIWw/PnAvbP/Z5XUVERJpL4aYDsgUGcMEpXQFYvG5vwwVTz4LrPoOkDCg/Ai9dDF/O1jgcERFp1RRuOqgJtbdjeH99LlU1joYLRifB1e/DkN+C4YBls2DR1VBZ4puKioiINJPCTQeV2asz8ZE2Csur+XzLwcYLB4WYdxAf/zhYg+DHt+G5LDi03Sd1FRERaQ6Fmw4qwGpx3UzznXX7jlMacxzOab+Hq9+DiARzReNnzoMtH3m5piIiIs2jcNOBObumlv2YR2llTdN26pFpjsPpPgwqC+GVS+GzR8DRSNeWiIiIDyncdGCDu0eT2jmM8mo7yzblNX3HqK5mC87Q3wEGfPJ3eP0qqCjyWl1FRESaSuGmA7NYLEyoXfPmnV8u6Hc8gcHwqydgwpMQEAw/vQvPjYSDW7xQUxERkaZTuOngnF1Tn285yJHSquYf4NQpMG0JRHaD/C3w7Pnw03serqWIiEjTKdx0cH3iIxjULYoah8H7G/a37CDdM8z7UqWcCVXF8NoV8PE/NA5HRET8QuFGXK03de411RwR8TDlHcj8g/n684fh1cugvODEKygiItIMCjfimhL+7Y7D7DlyAjfJDAiCcQ/BhU9DYAhs/RCePQ8ObPJQTUVERI5P4UboFhPK8F6dAbjltbVUVNtP7IBpl8HvPoToZDj8Mzw7Eja+feIVFRERaQKFGwHg7xeeTFRIIKtyjjDzzfUYJ3r/qG7p5no4Pc+B6lJYNBWW3g32ao/UV0REpCEKNwJA7y4R/PvKDAKsFt5as5d/f+qBWyuEd4bfvgXDbzJff/VPeOECOJJz4scWERFpgMKNuJzVN457JwwC4JEPN7OkpbOnjhUQCGP+Ab9ZALYo2LMS5p2tbioREfEahRtx89vTU7j6jFQA/rhwHRv2FnrmwIMuhD98AUlDzds2LJoK/7sVqss9c3wREZFaCjdSx9/GD+Cck7pQXm3n9y9+R15RhWcOHJsKv1sCZ/0RsMCqF8ybb2o2lYiIeJDCjdQRGGBlzhVD6BsfQV5RJde8+D3lVSc4g8opIAiy7oGr3oTw+Nq7i58L38+HEx3ELCIigsKNNCAqJIjnp55Gp/Bg1u8t5E+L1uJweDB89D4fbvgKeo+Emgp4949mV5UW/RMRkROkcCMN6tE5jHm/zSAowML763OZvczDN8WMiIcr34BR94M1EH58xxxsvHulZ88jIiIdisKNNGpYz048cOEpAPzr4228s3avZ09gtcKZ/we/+8gck1O4C+aPhc8fBYeHusJERKRDUbiR4/rN0GSuH9ELgD+/8QOrco54/iTdM+D6L+DkS8Cww8f3w38vhOJcz59LRETaNYUbaZLbx/Rn1MAEqmocXP/f70/sHlQNCYmCi5+DiXMhKAx2fAZPnQlbl3r+XCIi0m4p3EiTWK0WZk9OZ0DXKPJLqrjmxe8pqazx/IksFhjyW/PWDQknQ1k+vHwJfHgn1FR5/nwiItLuKNxIk4XbAnl+6lC6RNr4KbeYW19bg92TM6iO1eUkuGY5DLvOfL1iDswfDYc8cFsIERFp1xRupFm6xYTy7JSh2AKtLNt0gIeW/OS9kwWFwAWPwGWvQGgs7FsDT58DP7zuvXOKiEibp3AjzZaeHMMjv0kD4JnPf2bhd7u8e8L+4+EPX0KPM6CqBN68Ft66ASpLvHteERFpkxRupEUmpHXjlpF9AbjzrQ188/Mh754wujtM/R+MuAMsVlj3CjwzAvb/4N3ziohIm6NwIy12a1ZffjW4KzUOgz+8tIqcQ6XePWFAIJw30ww5kd3g0DZ4biR8M0+3bhAREReFG2kxi8XCo79JIy05hoKyan634DsKy6u9f+LUs8xbN5w0DuxVsOR2ePVyKM33/rlFRKTVU7iRExISFMCzV2XQNTqE7QdLuemV1dTYHd4/cVgnuPxVGPcwBATDlg9g9mBYMhMKdnv//CIi0mop3MgJi48K4dkpQwkNCuCLrfnc9+6PvjmxxQKZ15tTxhMHQ3UpfPNv+Fc6vHk95PmoHiIi0qr4PdzMnTuX1NRUQkJCyMzMZOXKhm+auHHjRi6++GJSU1OxWCzMnj3bdxWVRp2cFM3sy9KxWOA/K3L4z4qdvjt518Fw/efw2/8HqWeDowZ+eA2eGg4vXwo5X2tMjohIB+LXcLNw4UJmzJjBrFmzWL16NWlpaYwZM4YDBw7UW76srIxevXrx4IMPkpiY6OPayvGMGZTIX8b0B+De//3I51sO+u7kFgv0yYKr34VrP4YBEwALbP0QXhgHz4+Gn94Dhw+6zERExK8shuG//9JmZmZy2mmnMWfOHAAcDgfJycncfPPN3HHHHY3um5qayq233sqtt97arHMWFRURHR1NYWEhUVFRLa26NMAwDG5b9AP/b/UeIkMCeevGM+kTH+GfyuRvgxVPwtpXzIHHAHEnwRn/B4MvhUCbf+olIiLN1pzf335ruamqqmLVqlVkZWUdrYzVSlZWFitWrPDYeSorKykqKnJ7iPdYLBYeuOhkhqV2oriiht+/+B1HSv10T6i4PvDrf8KtG+CsP4ItGvK3wOKb4J9p8NW/oEJ/H0RE2hu/hZv8/HzsdjsJCQlu2xMSEsjNzfXYebKzs4mOjnY9kpOTPXZsqZ8tMICnfnsqyZ1CyTlUxvUvraKqxo/dQZEJkHUP/HEDjLoPIhKheD8svQueOBmW3QvFef6rn4iIeJTfBxR728yZMyksLHQ9du/WNGFf6Bxh4/mppxFpC2TljsP87e31+LEH1BQSBWfeArf+ABPmQOe+UFkIXz4Os0+B/92iG3OKiLQDfgs3cXFxBAQEkJfn/j/mvLw8jw4WttlsREVFuT3EN05KiOTJK4ZgtcDr3+/h6c9/9neVTIE2OPUqmL4SJr8M3U8DeyWsWgBPZsDrU2Dvan/XUkREWshv4SY4OJiMjAyWL1/u2uZwOFi+fDnDhw/3V7XEw87tF89dvxoIwIMf/MQfF671zSrGTWG1woBfwe+XwtXvQ98xgAE/vgPPngcLfgXblmkauYhIGxPoz5PPmDGDqVOnMnToUIYNG8bs2bMpLS1l2rRpAEyZMoWkpCSys7MBcxDyjz/+6Hq+d+9e1q5dS0REBH369PHbdUjjrj4jleKKGmYv28Jba/by7c+HeOzSdIb37uzvqpksFkg903zkbTQHGm94A3Z+YT4ST4HhN0GX/mCLBFuU+TPQZu4rIiKtil+nggPMmTOHRx55hNzcXNLT0/nXv/5FZmYmAOeeey6pqaksWLAAgJ07d9KzZ886xxgxYgSffvppk86nqeD+syrnCDNeX0vOoTIsFrjmrJ78aXQ/QoIC/F21ugp2w4q5sPpFqC6rv4w1sDbsHBN4nI/giPq32yLqL2tthX8GIiKtSHN+f/s93Piawo1/lVbW8Pf3NvHqyl0A9EuI5InJ6Qzs1ko/i7LD8N1zsPEtKC+AymKoKvb8eUJiIK6vuQ5P5z7mz7iToFNPCAjy/PlERNoYhZtGKNy0Dss35XH7//uB/JIqggIszBjVj+vO6UWAtQ108zgcUFViBh3no6rY/XVlMVQW1f4saWB7MTiOM/7IEmAGnF+Gnri+5s1DRUQ6CIWbRijctB6HSiqZ+eZ6PvrRnDF3Wmosj1+aTnKnMD/XzIdqKs2QU5wLh7ZCvvOxxfxZXdrwvmGdzenszhYf58+YFAjw63A6ERGPU7hphMJN62IYBotW7eHexRsprbITHhzArAmD+E1GdywdfbCuYZiLDTqDjjP0HNoGhY2s12QNgk693ENP/EDzERjsu/qLiHiQwk0jFG5ap92Hy5jx+lq+23kEgDGDEnjgwlPoHKH7P9WrqtQMOW6hZ6t5P62a8vr3CbBB4snQ7VToNgSSTjXDjwYzi0gboHDTCIWb1svuMHjm8595fOlmqu0GcRE2Hr7kFM7vn3D8ncXkcEDRnl90b22B3PVQUVC3fFA4dE0zg063IeajUy9NcRfxNYcd9q4y19bauhRK8qD3eTBwEvQcoVZXFG4apXDT+m3cV8gfF65lS14JAFdk9uDOCwYQbtM4khYzDDiyw1x5ed+a2sfa+sf0hMQcDTrO0BOVpMAj4mlF+2H7cjPQbP+k/v+AgHnT3/4XwMCJ0Os8CArxaTVbC4WbRijctA0V1XYe/XAzz325A4DUzmE8PjmdU3vE+rlm7YjDbrbq7FtTG3pWmy089nru4h4ef0zrzqnm8/A439dZpC2rqYLd35phZttyyFvv/n5INPQ+H/pkQWRX2Pw+bPqf2YrjFBwBJ401g06fLAjuOBMwFG4aoXDTtny9LZ/bFq1jX2EFVgtMP68P/zeyL0EB7f6er/5RUwUHfjSDzr41sHeN+dqw1y0bnXy0dSf1bPO5xu/4TtlhyPkKEgaZXYnSOh3JMVtnti6DHZ+Zy0i4WMzvTZ8s85GUUXemo8MOu1eat4X58R0o3nf0vaAw6DsaBk4wbx9ji/DJJTXKMKBon7n4aVxfjx5a4aYRCjdtT2F5Nfcs3shba/YCcEpSNE9MTqdPfCv4IncEVWWQt+GYLq3V5ngefvFPR2gn8x/ovqOg90gIbyW312hPHA74+RNY81/46b2jrWwpZ0L6Feb/5m2R/q1jR1ddbobObbXdTflb3N8PizsaZnqf17wWUIfDHJfz49vw42Io3HX0vcAQ85gDJ8JJY8xWIG8yDCjaCwd+goM/wcFNcHCz+agsMv8NuOpNj55S4aYRCjdt17s/7OPOtzZQWF6NLdDKXy8YwFWnp2BtCwv/tTcVRbB/nRl09nwHP38OlYXHFLCYLTp9Rpn/s+w2xLxRqbRMwS5Y8zKsfdl9GYDYVLNlwBk0g8LMX27pV0DKWfoz9wXDgEPba7ualsHOL91nLFoCIHkY9Blpho/ENM98LoYB+9earTkb3zbH1DkFBJtjcwZOhH7jTmzBT8OAwj21AeanY8LM5oZXa7cGmoF76uKWn7ceCjeNULhp23ILK/jzG+v4Yms+AGf3jeORS9JIjO6YA+xaDXu12XS+banZ/P7LsQRhnc3/yalVp+mqK+Cnd81Wmp8/wxVgQqJh8GQY8ltzplvhXvhhoRl8Dm07un9MD0i7AtIuM1e5bk1KDpqtGwFBZvhta7cYqamq7Wpaagaaghz39yO7mWGm7yhzplNojHfrYxhm66qz6+rY1iJroFmHgROh//iGW4qcMy1d4eXYEFNS/z7WQHPl9C79oMsAiO9v3mC4U2+vzO5SuGmEwk3bZxgG//0mhwfe30RFtYPo0CD+ceHJ/GpwN39XTZyK9h2d0vrzp2YztYvFHFvQd5TZsqNWHXe562H1f83AcuzsmZ4j4NQp0P9X9c+WMQyzFW3ty7DhTfc/85Szjum28kN3bmm+2aKx80vY+YX5S9MpKgkyr4eMq73flXKiyg7Dqhdg5bPmAptOAcHQY/jR7qb4Af6dXXjgp6NB58DGo9stVkg9y/x7EJ1ctyWmoRXRrYHmauhd+pnX5gwznXr5dIq6wk0jFG7aj20HSpjx+lp+2GN2h5zZpzOXndaDUQMTWuedxjsqt1adpeb/MI8V1rn2l8Ioc6ZIR2zVKS+A9YtgzUtmV4NTVBKkXwlDrjS7oJqqutwck7PmJTNcurqtwmHQJDPo9DjDe6Gy9BDkfHk00Bz4sW6Z+EFQehBKD5ivgyPM8Jb5B4hN8U69Wip/K3zzb1j76tEup/B4cyBvn1FmYGgNg3nrk7/1aNDJ/aHxstYgsyXG2QLTpb8ZZjr1ahWtawo3jVC4aV+q7Q6e/Hgbcz/Zht1h/lWODg3iwiFJ/GZodwZ1a+X/E+yImtSqMxr6ZkHXE2jVcdihpsK8f5e96hc/K82uhZoK83/dMT0gqptvZ3s5HGYAWP1f2LTYrAuYv2D6j4dTrzLHTZxonQr3wLrXYO0rcHj70e0xKWbISbv8xMOEc+bWji9qw8zGumXiB5qz6lLPMsdjhHc2u97WL4IVc4625lisZsvC8Juhe8aJ1etEGIb59/Obf8PWj45uTzwFTp8OJ18EgW1sBfXDO8y/a5veNVc579KvNsA4u5NaR4hpiMJNIxRu2qfdh8tY9P1u3li1h32FFa7tg7pFcenQZCamdyMmTCt8tjr2anPdD+fYhTqtOnGQeqbZLF5fSDk2qNgr3d+rb/p6Y6xBEN3d/EUfk3L0p/N5eBfPdDUU7jWDxtqX4MjOo9vjB8KQq8zxNN5ovTIM88967cuw4S33waCpZ5stRAMnQHD48Y9Vdhhyvj7aMpO3gTqz57oMMINMz7Nrw0wjs4IMw5xdtOLJ2pamWj2Gw/CbzEGxvgqezsD1zVPHhDSLWYfTbzSvSQta+oXCTSMUbto3u8Pgq235LPx+N0s35lFldwAQHGBl9KAEJp+WzJm94zTDqrUq3Fs762QpbP+04dkYzWYx/5cdYDN/BtrMFptAm7keR+EecNQ0foigMLOFxxV8eriHoMYGjdZUwZYPzFaa7cvBMP9eYouCky82Q03Sqb77pVlVVjtY+SXY8TmuYBIcYS73n34FpJxxtD7lRyBnhTleZucXkFtPmInrZwaZ1LPMMT4RXVpWt9z1sGIurH8DHNXmtk69zGCRfkXTwldLlByE75+H754zu8vA7MYbcqXZVda5t3fOK02mcNMIhZuO40hpFe+s3cvC7/ewaf/Rro+kmFAuzujObzK6k9yp46zu2eY4W3X2rTFbbpxhJMBmDmJ0+2mr571jAow1sPHg4LCb3WUFOebUatfPXebzon3U+WX+SyHRtS09PczxMTEpEJ1ktnCsew3K8o+WTTnTDDQDJ/p/hdmCXbCudrbVsdOJY1PNQcz718L+H6gbZk4yg4yzqyki3rP1KtoPK5+G7+dDRe0yA6GxMPR3MOw6iEz0zHnyNppdTz8sMlv/wBzrNOw6yJhqnlNaBYWbRijcdEwb9hby+ve7eXvNXooqjv4P/cw+nbl0aDJjBiVqELI0rKbSbN05srNu8DmS4x5cGhKRCOmXm6GmNbYCGAbsWmGGnI1v153+27lvbZipDTSRPrqhbWWJ2Y33zdyj3XgBwXDKb8wuq4SBzT+mw2G2EH4z170bLCnDbCEaOLFVjz3pqBRuGqFw07FVVNv56Mc8Xv9uN19tz8f5tz8yJJCJ6d24dGgypyRFY1GfujRHZcnRsFOw62jrT8Eus9sq/bfmjLBfLq3fWlWVmivg5m0wp+qnnAlRXf1bJ4fdnAG2Yo7ZoufU+3wz5PQ+//jdelVlsO5V+Hbe0bVgLFYY8GtzkHDyMI2nacUUbhqhcCNOuw+X8f9W72HR93vYW3B0RdH+iZFcOjSZSUOS6BSuQcgirc7u78zBx5v+d3T8UvwgGD4dTrmk7iymov2w8hlzjZryI+Y2W5Q59XzYda1v6rnUS+GmEQo38ksOh8GKnw/x+ve7+WBDLlU15j+WQQEWRg1M4DdDkzmnbxcCNAhZpHU5stOc1bT6v0cXoItIMAPL0N+ZLWff/Ntc1NA5ODkmBU6/wZwdFqLfAW2Jwk0jFG6kMYVl1Sxet5fXv9/D+r1H75WUEGXjzD5xZKTEkpESS9/4SIUdkdai/AisWgDfPn105eCA4KM3FgVzWvnpN5prCOnu9W2Swk0jFG6kqX7cV2QOQl67l4Kyarf3Im2BpPeI4dQeZthJ7xFDVIgGIIr4VU0VbHzL7LLKXW/Okht0oRlqkk71d+3kBCncNELhRpqrssbOiu2HWJ1zhFW7jrB2VwGlVe4LxFks0C8hklNTYl2BJ7VzmAYmi/iDYcCBTebdsD01ZVz8TuGmEQo3cqLsDoPNucWs2nXEDDw5R9h1uKxOuU7hwa6gk5ESy+Du0ZpuLiLSQgo3jVC4EW84UFzB6pwCVu8yw876vYWugclOgVYLg7pFcWrK0cDTNTrUTzUWEWlbFG4aoXAjvlBZY2fjviJXy873OUc4WFxZp1y36BCGpMSS3j2GlM5hpHQOJ7lTKGHBbWQ9FBERH1G4aYTCjfiDYRjsOVLOamdX1q4jbNpf7LqT+S91ibSR0imMHp3C6NHZ/JnSOYzkTmF0ibBpLI+IdDgKN41QuJHWorSyhnV7ClidYwadnMOl5Bwqo7ii8Rs4hgYF1Bt6UjqFkRQbii1Q43pEpP1RuGmEwo20dgVlVew6XEbOoTJ2HS5jl/Pn4TL2FZbT2DfWYoFu0aEkdwolpVO4KwCdlBBJ3/gI3Q1dRNoshZtGKNxIW1ZV42DPkaNhZ9ehMnIOl7G7NgyVV9sb3DcqJJBTU2IZmhJLRkon0pNjCA1WK4+ItA0KN41QuJH2yjAM8kuq2HW41K3lJ+dQGT/uK6oTfAKtFgYlRTPUGXhSY4mPDPFT7UVEGqdw0wiFG+mIqu0ONu0v4vudztlbh8krqjt7q0enMDPspHZiaGosfbqoK0tEWgeFm0Yo3Igcnb3lDDrf7zzC5rziOuN5okODOLVHDENTO5GREktad3VliYh/KNw0QuFGpH6F5dWsqV2E8PudR1i7u6DBrqzTUmIZmmqO3ekSafNTjUWkI1G4aYTCjUjTHNuV5WzdOVDPQoQptTOyOocH0znCRueIYPN5uPk8LsJGp/BgwoIDtD6PiLSYwk0jFG5EWsbZleUMOqty6u/KakhIkNUVeFxBKDy49rXN7Wen8GDdh0tE3CjcNELhRsRzCsurWb+nkLyiCg6VVnKopIr8kioOl1ZyqLSq9nUllb+4z1ZTRNgCXUEnMiSIyJBAIm2B5s+QICKOeW7+dN+ucCTSvjTn97duYCMiLRYdGsRZfeMaLWMYBmVVdjPolFZyuKSKQ6WV5JeY4ccZhMzXlRwuraLGYVBSWUNJZQ05h+recb0pggOsRIYEEuEMPrYg1/OoY0JQREggEbZAwoIDCbcFuJ5H2MzX4cGBmjEm0sYo3IiIV1ksFsJtgYTbAunROey45Q3DoKi8xgxCta0/xRXVlFTWUFxR43peVGG+Lqmort1e4wpEAFV2h9l6VFp1wtcQGhRAuC2QCFuAW/AJswUSERxYe30BrusMD3aWDyQ0OICw4ABCg2oftc8DA6wnXC8RqZ/CjYi0KhaLheiwIKLDgujdpfn7249p9SmuDT4lFTUUOZ//YntxZQ2llTWUVtnNn8e8dt7YtLzaTnm1nfwSz11nUIDFLeyEBgcSGmStfR1Y+9NKWLDZxWaWsdaWM1+H1QancFug62e4LZCwoAC1NkmHpnAjIu1KgNVCdGgQ0aFBQGiLj2MYBpU1jtqwY6e0ygw9JZU1lFXZKakNQc7nZZU1lFTWBqSqGtd+ZdU1lFc5qKi2U1ZVg/NG8NV2g2q72QLlDWZrkzP4mK1JYce0KtV9HUhYbTecMyiFBgcQZLUSGGAh0GohwGohMMBKoNVSu82K1YJmwUmro3AjIlIPi8VCSFAAIUEBdI7wzDENw6DK7qCiylEbeuyUVdmpqG0Zcj2v3V5eba8NRebz8qrah7NMtRmuyo4JVc7wdLS16cS75Y4nKKA2+BwThAKt1towdPR1/SHJSlDttqAAs0yA1eIKVUEBR48TVHvMoIBj9nceI8BCgNX86TxXgMWC1Wp+lgEWC1aLBasFrNajzy0W83xWC7XbzH1cZV37WbBYqC1b/3Gsx7xvqd03oPa5AqBvtYpwM3fuXB555BFyc3NJS0vjySefZNiwYQ2WX7RoEXfddRc7d+6kb9++PPTQQ1xwwQU+rLGISPNZLBZsgQHYAgOIJsjjxz+2tcnVolRV24JUdWwL1DGva0NRWW23nPOnc5vdYVDjMKixO1zB6ZfMVigDaP6suI7il2HHLTwdE67MsAUBlqOhLSjASnCgGeacz4Nq3wsMsBJ8TLmgXzw/dr+gQCvBx4Q/Z2gLODYAWjlme22gc9WxbhB0vXa+X3t9IUEBfl3g0+/hZuHChcyYMYN58+aRmZnJ7NmzGTNmDJs3byY+Pr5O+a+//prLL7+c7OxsfvWrX/HKK68wadIkVq9ezcknn+yHKxARaR3cWpu8cHyHw8BuGNTYDWocjtqfBnaHQbXdcTQIub139LkzJB27T4299nltuWOP43xebT+6X42rnHnsaucx7UfP7SzvMMBhGObDcczz2u2GYY7R+uVzh2EGRbth4HCY7zmcr53vOYwGw159DAPshoGdjrH6ypAeMbx145l+O7/f17nJzMzktNNOY86cOQA4HA6Sk5O5+eabueOOO+qUnzx5MqWlpbz77ruubaeffjrp6enMmzfvuOfTOjciIuIpxjFhyX5MEGooULmeO34ZtMznzoDlDHXOAFhtd1Bld1DtfNSYgbC6xgxzzvfqlK0xX1c7jNqy5nuuIOdwD26OY87vDGSO2pDnuoba5/baejuDnmtfwyA9OYbXrhvu0T/rNrPOTVVVFatWrWLmzJmubVarlaysLFasWFHvPitWrGDGjBlu28aMGcPbb79db/nKykoqK48uGV9UVHTiFRcREcHZnQMBWNC6ka2HXxdayM/Px263k5CQ4LY9ISGB3NzcevfJzc1tVvns7Gyio6Ndj+TkZM9UXkRERFqldr+K1MyZMyksLHQ9du/e7e8qiYiIiBf5tVsqLi6OgIAA8vLy3Lbn5eWRmJhY7z6JiYnNKm+z2bDZ/DdiW0RERHzLry03wcHBZGRksHz5ctc2h8PB8uXLGT68/oFIw4cPdysPsHTp0gbLi4iISMfi96ngM2bMYOrUqQwdOpRhw4Yxe/ZsSktLmTZtGgBTpkwhKSmJ7OxsAG655RZGjBjBY489xvjx43nttdf4/vvveeaZZ/x5GSIiItJK+D3cTJ48mYMHD3L33XeTm5tLeno6S5YscQ0a3rVrF1br0QamM844g1deeYW//e1v/PWvf6Vv3768/fbbWuNGREREgFawzo2vaZ0bERGRtqc5v7/b/WwpERER6VgUbkRERKRdUbgRERGRdkXhRkRERNoVhRsRERFpVxRuREREpF1RuBEREZF2xe+L+Pmac1mfoqIiP9dEREREmsr5e7spy/N1uHBTXFwMQHJysp9rIiIiIs1VXFxMdHR0o2U63ArFDoeDffv2ERkZicVi8eixi4qKSE5OZvfu3e1+9WNda/vVka5X19p+daTr7SjXahgGxcXFdOvWze22TPXpcC03VquV7t27e/UcUVFR7fov2LF0re1XR7peXWv71ZGutyNc6/FabJw0oFhERETaFYUbERERaVcUbjzIZrMxa9YsbDabv6vidbrW9qsjXa+utf3qSNfbka61qTrcgGIRERFp39RyIyIiIu2Kwo2IiIi0Kwo3IiIi0q4o3IiIiEi7onDTTHPnziU1NZWQkBAyMzNZuXJlo+UXLVpE//79CQkJ4ZRTTuH999/3UU1bLjs7m9NOO43IyEji4+OZNGkSmzdvbnSfBQsWYLFY3B4hISE+qvGJueeee+rUvX///o3u0xY/V4DU1NQ612qxWJg+fXq95dvS5/r555/z61//mm7dumGxWHj77bfd3jcMg7vvvpuuXbsSGhpKVlYWW7duPe5xm/ud95XGrre6uprbb7+dU045hfDwcLp168aUKVPYt29fo8dsyXfBF4732V599dV16j127NjjHrc1frbHu9b6vr8Wi4VHHnmkwWO21s/VmxRummHhwoXMmDGDWbNmsXr1atLS0hgzZgwHDhyot/zXX3/N5Zdfzu9//3vWrFnDpEmTmDRpEhs2bPBxzZvns88+Y/r06XzzzTcsXbqU6upqRo8eTWlpaaP7RUVFsX//ftcjJyfHRzU+cYMGDXKr+5dfftlg2bb6uQJ89913bte5dOlSAH7zm980uE9b+VxLS0tJS0tj7ty59b7/8MMP869//Yt58+bx7bffEh4ezpgxY6ioqGjwmM39zvtSY9dbVlbG6tWrueuuu1i9ejVvvvkmmzdvZsKECcc9bnO+C75yvM8WYOzYsW71fvXVVxs9Zmv9bI93rcde4/79+5k/fz4Wi4WLL7640eO2xs/VqwxpsmHDhhnTp093vbbb7Ua3bt2M7Ozsestfeumlxvjx4922ZWZmGtdff71X6+lpBw4cMADjs88+a7DMCy+8YERHR/uuUh40a9YsIy0trcnl28vnahiGccsttxi9e/c2HA5Hve+31c8VMN566y3Xa4fDYSQmJhqPPPKIa1tBQYFhs9mMV199tcHjNPc77y+/vN76rFy50gCMnJycBss097vgD/Vd69SpU42JEyc26zht4bNtyuc6ceJE4/zzz2+0TFv4XD1NLTdNVFVVxapVq8jKynJts1qtZGVlsWLFinr3WbFihVt5gDFjxjRYvrUqLCwEoFOnTo2WKykpISUlheTkZCZOnMjGjRt9UT2P2Lp1K926daNXr15ceeWV7Nq1q8Gy7eVzraqq4qWXXuJ3v/tdozeRbcufq9OOHTvIzc11+9yio6PJzMxs8HNryXe+NSssLMRisRATE9NoueZ8F1qTTz/9lPj4ePr168cNN9zAoUOHGizbXj7bvLw83nvvPX7/+98ft2xb/VxbSuGmifLz87Hb7SQkJLhtT0hIIDc3t959cnNzm1W+NXI4HNx6662ceeaZnHzyyQ2W69evH/Pnz+edd97hpZdewuFwcMYZZ7Bnzx4f1rZlMjMzWbBgAUuWLOGpp55ix44dnH322RQXF9dbvj18rgBvv/02BQUFXH311Q2Wacuf67Gcn01zPreWfOdbq4qKCm6//XYuv/zyRm+s2NzvQmsxduxY/vOf/7B8+XIeeughPvvsM8aNG4fdbq+3fHv5bF988UUiIyO56KKLGi3XVj/XE9Hh7gouzTN9+nQ2bNhw3P7Z4cOHM3z4cNfrM844gwEDBvD0009z//33e7uaJ2TcuHGu54MHDyYzM5OUlBRef/31Jv2PqK16/vnnGTduHN26dWuwTFv+XMVUXV3NpZdeimEYPPXUU42Wbavfhcsuu8z1/JRTTmHw4MH07t2bTz/9lJEjR/qxZt41f/58rrzyyuMO8m+rn+uJUMtNE8XFxREQEEBeXp7b9ry8PBITE+vdJzExsVnlW5ubbrqJd999l08++YTu3bs3a9+goCCGDBnCtm3bvFQ774mJieGkk05qsO5t/XMFyMnJYdmyZVxzzTXN2q+tfq7Oz6Y5n1tLvvOtjTPY5OTksHTp0kZbbepzvO9Ca9WrVy/i4uIarHd7+Gy/+OILNm/e3OzvMLTdz7U5FG6aKDg4mIyMDJYvX+7a5nA4WL58udv/bI81fPhwt/IAS5cubbB8a2EYBjfddBNvvfUWH3/8MT179mz2Mex2O+vXr6dr165eqKF3lZSUsH379gbr3lY/12O98MILxMfHM378+Gbt11Y/1549e5KYmOj2uRUVFfHtt982+Lm15DvfmjiDzdatW1m2bBmdO3du9jGO911orfbs2cOhQ4carHdb/2zBbHnNyMggLS2t2fu21c+1Wfw9orktee211wybzWYsWLDA+PHHH43rrrvOiImJMXJzcw3DMIyrrrrKuOOOO1zlv/rqKyMwMNB49NFHjU2bNhmzZs0ygoKCjPXr1/vrEprkhhtuMKKjo41PP/3U2L9/v+tRVlbmKvPLa7333nuNDz/80Ni+fbuxatUq47LLLjNCQkKMjRs3+uMSmuVPf/qT8emnnxo7duwwvvrqKyMrK8uIi4szDhw4YBhG+/lcnex2u9GjRw/j9ttvr/NeW/5ci4uLjTVr1hhr1qwxAOPxxx831qxZ45od9OCDDxoxMTHGO++8Y/zwww/GxIkTjZ49exrl5eWuY5x//vnGk08+6Xp9vO+8PzV2vVVVVcaECROM7t27G2vXrnX7HldWVrqO8cvrPd53wV8au9bi4mLjtttuM1asWGHs2LHDWLZsmXHqqacaffv2NSoqKlzHaCuf7fH+HhuGYRQWFhphYWHGU089Ve8x2srn6k0KN8305JNPGj169DCCg4ONYcOGGd98843rvREjRhhTp051K//6668bJ510khEcHGwMGjTIeO+993xc4+YD6n288MILrjK/vNZbb73V9eeSkJBgXHDBBcbq1at9X/kWmDx5stG1a1cjODjYSEpKMiZPnmxs27bN9X57+VydPvzwQwMwNm/eXOe9tvy5fvLJJ/X+vXVej8PhMO666y4jISHBsNlsxsiRI+v8GaSkpBizZs1y29bYd96fGrveHTt2NPg9/uSTT1zH+OX1Hu+74C+NXWtZWZkxevRoo0uXLkZQUJCRkpJiXHvttXVCSlv5bI/399gwDOPpp582QkNDjYKCgnqP0VY+V2+yGIZheLVpSERERMSHNOZGRERE2hWFGxEREWlXFG5ERESkXVG4ERERkXZF4UZERETaFYUbERERaVcUbkRERKRdUbgRkQ7PYrHw9ttv+7saIuIhCjci4ldXX301FoulzmPs2LH+rpqItFGB/q6AiMjYsWN54YUX3LbZbDY/1UZE2jq13IiI39lsNhITE90esbGxgNll9NRTTzFu3DhCQ0Pp1asXb7zxhtv+69ev5/zzzyc0NJTOnTtz3XXXUVJS4lZm/vz5DBo0CJvNRteuXbnpppvc3s/Pz+fCCy8kLCyMvn37snjxYu9etIh4jcKNiLR6d911FxdffDHr1q3jyiuv5LLLLmPTpk0AlJaWMmbMGGJjY/nuu+9YtGgRy5YtcwsvTz31FNOnT+e6665j/fr1LF68mD59+rid49577+XSSy/lhx9+4IILLuDKK6/k8OHDPr1OEfEQf9+5U0Q6tqlTpxoBAQFGeHi42+Mf//iHYRjmXer/8Ic/uO2TmZlp3HDDDYZhGMYzzzxjxMbGGiUlJa7333vvPcNqtbruDN2tWzfjzjvvbLAOgPG3v/3N9bqkpMQAjA8++MBj1ykivqMxNyLid+eddx5PPfWU27ZOnTq5ng8fPtztveHDh7N27VoANm3aRFpaGuHh4a73zzzzTBwOB5s3b8ZisbBv3z5GjhzZaB0GDx7seh4eHk5UVBQHDhxo6SWJiB8p3IiI34WHh9fpJvKU0NDQJpULCgpye22xWHA4HN6okoh4mcbciEir980339R5PWDAAAAGDBjAunXrKC0tdb3/1VdfYbVa6devH5GRkaSmprJ8+XKf1llE/EctNyLid5WVleTm5rptCwwMJC4uDoBFixYxdOhQzjrrLF5++WVWrlzJ888/D8CVV17JrFmzmDp1Kvfccw8HDx7k5ptv5qqrriIhIQGAe+65hz/84Q/Ex8czbtw4iouL+eqrr7j55pt9e6Ei4hMKNyLid0uWLKFr165u2/r168dPP/0EmDOZXnvtNW688Ua6du3Kq6++ysCBAwEICwvjww8/5JZbbuG0004jLCyMiy++mMcff9x1rKlTp1JRUcETTzzBbbfdRlxcHJdcconvLlBEfMpiGIbh70qIiDTEYrHw1ltvMWnSJH9XRUTaCI25ERERkXZF4UZERETaFY25EZFWTT3nItJcarkRERGRdkXhRkRERNoVhRsRERFpVxRuREREpF1RuBEREZF2ReFGRERE2hWFGxEREWlXFG5ERESkXVG4ERERkXbl/wMrcaZUnMJhzQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:02:52.142655Z","iopub.execute_input":"2024-06-06T18:02:52.143003Z","iopub.status.idle":"2024-06-06T18:02:52.404821Z","shell.execute_reply.started":"2024-06-06T18:02:52.142970Z","shell.execute_reply":"2024-06-06T18:02:52.403903Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:02:52.406125Z","iopub.execute_input":"2024-06-06T18:02:52.406499Z","iopub.status.idle":"2024-06-06T18:03:41.821603Z","shell.execute_reply.started":"2024-06-06T18:02:52.406465Z","shell.execute_reply":"2024-06-06T18:03:41.820641Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 787ms/step - accuracy: 1.0000 - loss: 0.0244\nTest Loss: 0.017047664150595665\nTest Accuracy: 1.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Inception","metadata":{}},{"cell_type":"code","source":"from keras.applications import InceptionV3\nfrom keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\n# Loading model\ninception_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=inception_model.input, outputs=inception_model.get_layer('mixed10').output)\n\n# Freezing convolutional layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Adding dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(rate=0.5)(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# binding model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1)\n\nmodel.summary() # model summary","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:04:03.853981Z","iopub.execute_input":"2024-06-06T18:04:03.854335Z","iopub.status.idle":"2024-06-06T18:04:07.313797Z","shell.execute_reply.started":"2024-06-06T18:04:03.854305Z","shell.execute_reply":"2024-06-06T18:04:07.312754Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_15\"\u001b[0m\n","text/html":"
Model: \"functional_15\"\n
\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_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m240\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m138,240\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ activation_10[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_11[\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;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_14[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_17[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_18[\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;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_21[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_24[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_25[\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;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m995,328\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_29[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_34[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_39[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_35[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_40[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_36 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_36[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_36[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_41[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_37 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_32[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_42 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_37[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_37[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_43[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_39 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_33[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_38[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_39[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_48[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_44 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_44[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_49[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_45 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_45[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_45[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_50[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_41 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_46 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_41[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_46[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_46[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_51[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_42 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_47 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_42[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_47[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_44[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_47[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_52[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_53[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_40 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_43 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_48 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_49 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_40[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_43[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_48[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_49[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_58 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_58[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_54 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_59 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_54[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_59[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_55 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_55 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_60 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_55[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_55[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_60[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_51 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_56 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_56 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_51[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_61 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_56[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_56[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_61[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_52 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_57 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_54 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_57 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_52[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_62 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_57[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_63 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_54[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_57[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_62[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_50 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_53 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_58 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_59 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_50[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_53[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_58[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_59[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_68 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_68[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_64 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_69 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_64[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_69[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_65 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_65 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_70 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_65[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_65[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_70[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_61 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_66 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_66 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_61[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_71 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_66[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_66[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_71[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_62 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_67 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_64 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_67 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_62[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_72 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_67[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_73 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_64[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_67[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_72[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_73[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_60 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_63 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_68 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_69 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_60[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_63[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_68[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_69[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_76 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_76[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_72 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_77 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_72[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_77[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_73 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_74 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_78 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_73[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_74[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_78[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_70 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_74 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_75 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m552,960\u001b[0m │ activation_70[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_79 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m331,776\u001b[0m │ activation_74[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_75[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_79[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_71 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_75 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_71[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ activation_75[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_84 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m573,440\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_84[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_80 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_81 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m491,520\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_85 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_80[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_81[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_85[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_77 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_81 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_82 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_83 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_86 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_87 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_80 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m409,600\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_82[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_83[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_86[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_87[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_88 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m245,760\u001b[0m │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_80[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_78 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_79 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_82 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_83 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_88[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_76 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_78[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_79[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_82[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_83[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_84 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_76[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ │ │ │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n│ │ │ │ activation_84[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_93 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m917,504\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_93[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_89 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_90 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_94 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_89[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_90[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_94[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_86 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_90 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_91 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_92 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_95 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_96 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_89 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m655,360\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_91[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_92[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_95[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_96[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_97 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m393,216\u001b[0m │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_89[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_87 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_88 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_91 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_92 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_97[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_85 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_87[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_88[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_91[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_92[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_93 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_85[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ │ │ │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_93[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m2,098,176\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_3       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (Conv2D)   │ (None, 111, 111,  │        864 │ input_layer_3[0]… │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 111, 111,  │         96 │ conv2d_4[0][0]    │\n│ (BatchNormalizatio… │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation          │ (None, 111, 111,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (Conv2D)   │ (None, 109, 109,  │      9,216 │ activation[0][0]  │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 109, 109,  │         96 │ conv2d_5[0][0]    │\n│ (BatchNormalizatio… │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_1        │ (None, 109, 109,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (Conv2D)   │ (None, 109, 109,  │     18,432 │ activation_1[0][… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 109, 109,  │        192 │ conv2d_6[0][0]    │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_2        │ (None, 109, 109,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d       │ (None, 54, 54,    │          0 │ activation_2[0][… │\n│ (MaxPooling2D)      │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (Conv2D)   │ (None, 54, 54,    │      5,120 │ max_pooling2d[0]… │\n│                     │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 54, 54,    │        240 │ conv2d_7[0][0]    │\n│ (BatchNormalizatio… │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_3        │ (None, 54, 54,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (Conv2D)   │ (None, 52, 52,    │    138,240 │ activation_3[0][… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 52, 52,    │        576 │ conv2d_8[0][0]    │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_4        │ (None, 52, 52,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1     │ (None, 25, 25,    │          0 │ activation_4[0][… │\n│ (MaxPooling2D)      │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (Conv2D)  │ (None, 25, 25,    │     12,288 │ max_pooling2d_1[… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_12[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_8        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (Conv2D)  │ (None, 25, 25,    │      9,216 │ max_pooling2d_1[… │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_8[0][… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_10[0][0]   │\n│ (BatchNormalizatio… │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_13[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_6        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_9        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d   │ (None, 25, 25,    │          0 │ max_pooling2d_1[… │\n│ (AveragePooling2D)  │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (Conv2D)   │ (None, 25, 25,    │     12,288 │ max_pooling2d_1[… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (Conv2D)  │ (None, 25, 25,    │     76,800 │ activation_6[0][… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_9[0][… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (Conv2D)  │ (None, 25, 25,    │      6,144 │ average_pooling2… │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_9[0][0]    │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_11[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_14[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │         96 │ conv2d_15[0][0]   │\n│ (BatchNormalizatio… │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_5        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_7        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_10       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_11       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed0              │ (None, 25, 25,    │          0 │ activation_5[0][… │\n│ (Concatenate)       │ 256)              │            │ activation_7[0][… │\n│                     │                   │            │ activation_10[0]… │\n│                     │                   │            │ activation_11[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (Conv2D)  │ (None, 25, 25,    │     16,384 │ mixed0[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_19[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_15       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (Conv2D)  │ (None, 25, 25,    │     12,288 │ mixed0[0][0]      │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_15[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_17[0][0]   │\n│ (BatchNormalizatio… │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_20[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_13       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_16       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_1 │ (None, 25, 25,    │          0 │ mixed0[0][0]      │\n│ (AveragePooling2D)  │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (Conv2D)  │ (None, 25, 25,    │     16,384 │ mixed0[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (Conv2D)  │ (None, 25, 25,    │     76,800 │ activation_13[0]… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_16[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (Conv2D)  │ (None, 25, 25,    │     16,384 │ average_pooling2… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_16[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_18[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_21[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_22[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_12       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_14       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_17       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_18       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed1              │ (None, 25, 25,    │          0 │ activation_12[0]… │\n│ (Concatenate)       │ 288)              │            │ activation_14[0]… │\n│                     │                   │            │ activation_17[0]… │\n│                     │                   │            │ activation_18[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed1[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_26[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_22       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (Conv2D)  │ (None, 25, 25,    │     13,824 │ mixed1[0][0]      │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_22[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_24[0][0]   │\n│ (BatchNormalizatio… │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_27[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_20       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_23       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_2 │ (None, 25, 25,    │          0 │ mixed1[0][0]      │\n│ (AveragePooling2D)  │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed1[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (Conv2D)  │ (None, 25, 25,    │     76,800 │ activation_20[0]… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_23[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (Conv2D)  │ (None, 25, 25,    │     18,432 │ average_pooling2… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_23[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_25[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_28[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_29[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_19       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_21       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_24       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_25       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed2              │ (None, 25, 25,    │          0 │ activation_19[0]… │\n│ (Concatenate)       │ 288)              │            │ activation_21[0]… │\n│                     │                   │            │ activation_24[0]… │\n│                     │                   │            │ activation_25[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_31 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed2[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_31[0][0]   │\n│ (BatchNormalizatio… │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_27       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_27[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_32[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_28       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (Conv2D)  │ (None, 12, 12,    │    995,328 │ mixed2[0][0]      │\n│                     │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (Conv2D)  │ (None, 12, 12,    │     82,944 │ activation_28[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │      1,152 │ conv2d_30[0][0]   │\n│ (BatchNormalizatio… │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        288 │ conv2d_33[0][0]   │\n│ (BatchNormalizatio… │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_26       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_29       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2     │ (None, 12, 12,    │          0 │ mixed2[0][0]      │\n│ (MaxPooling2D)      │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed3              │ (None, 12, 12,    │          0 │ activation_26[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_29[0]… │\n│                     │                   │            │ max_pooling2d_2[… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_38 (Conv2D)  │ (None, 12, 12,    │     98,304 │ mixed3[0][0]      │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_38[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_34       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_39 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_34[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_39[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_35       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (Conv2D)  │ (None, 12, 12,    │     98,304 │ mixed3[0][0]      │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_40 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_35[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_35[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_40[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_31       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_36       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_31[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_41 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_36[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_36[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_41[0][0]   │\n│ (BatchNormalizatio… │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_32       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_37       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_3 │ (None, 12, 12,    │          0 │ mixed3[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed3[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (Conv2D)  │ (None, 12, 12,    │    172,032 │ activation_32[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_42 (Conv2D)  │ (None, 12, 12,    │    172,032 │ activation_37[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_43 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_34[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_37[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_42[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_43[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_30       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_33       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_38       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_39       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed4              │ (None, 12, 12,    │          0 │ activation_30[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_33[0]… │\n│                     │                   │            │ activation_38[0]… │\n│                     │                   │            │ activation_39[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_48 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed4[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_48[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_44       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_49 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_44[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_49[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_45       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_45 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed4[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_50 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_45[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_45[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_50[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_41       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_46       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_46 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_41[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_51 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_46[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_46[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_51[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_42       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_47       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_4 │ (None, 12, 12,    │          0 │ mixed4[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_44 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed4[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_47 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_42[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_52 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_47[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_53 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_44[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_47[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_52[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_53[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_40       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_43       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_48       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_49       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed5              │ (None, 12, 12,    │          0 │ activation_40[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_43[0]… │\n│                     │                   │            │ activation_48[0]… │\n│                     │                   │            │ activation_49[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_58 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed5[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_58[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_54       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_59 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_54[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_59[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_55       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_55 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed5[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_60 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_55[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_55[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_60[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_51       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_56       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_56 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_51[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_61 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_56[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_56[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_61[0][0]   │\n│ (BatchNormalizatio… │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_52       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_57       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_5 │ (None, 12, 12,    │          0 │ mixed5[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_54 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed5[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_57 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_52[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_62 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_57[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_63 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_54[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_57[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_62[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_63[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_50       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_53       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_58       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_59       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed6              │ (None, 12, 12,    │          0 │ activation_50[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_53[0]… │\n│                     │                   │            │ activation_58[0]… │\n│                     │                   │            │ activation_59[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_68 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_68[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_64       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_69 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_64[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_69[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_65       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_65 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_70 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_65[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_65[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_70[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_61       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_66       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_66 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_61[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_71 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_66[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_66[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_71[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_62       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_67       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_6 │ (None, 12, 12,    │          0 │ mixed6[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_64 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_67 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_62[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_72 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_67[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_73 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_64[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_67[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_72[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_73[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_60       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_63       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_68       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_69       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed7              │ (None, 12, 12,    │          0 │ activation_60[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_63[0]… │\n│                     │                   │            │ activation_68[0]… │\n│                     │                   │            │ activation_69[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_76 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed7[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_76[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_72       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_77 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_72[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_77[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_73       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_74 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed7[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_78 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_73[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_74[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_78[0][0]   │\n│ (BatchNormalizatio… │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_70       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_74       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_75 (Conv2D)  │ (None, 5, 5, 320) │    552,960 │ activation_70[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_79 (Conv2D)  │ (None, 5, 5, 192) │    331,776 │ activation_74[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_75[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_79[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_71       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_75       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (None, 5, 5, 768) │          0 │ mixed7[0][0]      │\n│ (MaxPooling2D)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed8              │ (None, 5, 5,      │          0 │ activation_71[0]… │\n│ (Concatenate)       │ 1280)             │            │ activation_75[0]… │\n│                     │                   │            │ max_pooling2d_3[… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_84 (Conv2D)  │ (None, 5, 5, 448) │    573,440 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 448) │      1,344 │ conv2d_84[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_80       │ (None, 5, 5, 448) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_81 (Conv2D)  │ (None, 5, 5, 384) │    491,520 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_85 (Conv2D)  │ (None, 5, 5, 384) │  1,548,288 │ activation_80[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_81[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_85[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_77       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_81       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_82 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_77[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_83 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_77[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_86 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_81[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_87 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_81[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_7 │ (None, 5, 5,      │          0 │ mixed8[0][0]      │\n│ (AveragePooling2D)  │ 1280)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_80 (Conv2D)  │ (None, 5, 5, 320) │    409,600 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_82[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_83[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_86[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_87[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_88 (Conv2D)  │ (None, 5, 5, 192) │    245,760 │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_80[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_78       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_79       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_82       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_83       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_88[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_76       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_0            │ (None, 5, 5, 768) │          0 │ activation_78[0]… │\n│ (Concatenate)       │                   │            │ activation_79[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate         │ (None, 5, 5, 768) │          0 │ activation_82[0]… │\n│ (Concatenate)       │                   │            │ activation_83[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_84       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9              │ (None, 5, 5,      │          0 │ activation_76[0]… │\n│ (Concatenate)       │ 2048)             │            │ mixed9_0[0][0],   │\n│                     │                   │            │ concatenate[0][0… │\n│                     │                   │            │ activation_84[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_93 (Conv2D)  │ (None, 5, 5, 448) │    917,504 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 448) │      1,344 │ conv2d_93[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_89       │ (None, 5, 5, 448) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_90 (Conv2D)  │ (None, 5, 5, 384) │    786,432 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_94 (Conv2D)  │ (None, 5, 5, 384) │  1,548,288 │ activation_89[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_90[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_94[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_86       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_90       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_91 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_86[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_92 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_86[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_95 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_90[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_96 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_90[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_8 │ (None, 5, 5,      │          0 │ mixed9[0][0]      │\n│ (AveragePooling2D)  │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_89 (Conv2D)  │ (None, 5, 5, 320) │    655,360 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_91[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_92[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_95[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_96[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_97 (Conv2D)  │ (None, 5, 5, 192) │    393,216 │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_89[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_87       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_88       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_91       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_92       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_97[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_85       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_1            │ (None, 5, 5, 768) │          0 │ activation_87[0]… │\n│ (Concatenate)       │                   │            │ activation_88[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1       │ (None, 5, 5, 768) │          0 │ activation_91[0]… │\n│ (Concatenate)       │                   │            │ activation_92[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_93       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed10             │ (None, 5, 5,      │          0 │ activation_85[0]… │\n│ (Concatenate)       │ 2048)             │            │ mixed9_1[0][0],   │\n│                     │                   │            │ concatenate_1[0]… │\n│                     │                   │            │ activation_93[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 2048)      │          0 │ mixed10[0][0]     │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_6 (Dense)     │ (None, 1024)      │  2,098,176 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_3 (Dropout) │ (None, 1024)      │          0 │ dense_6[0][0]     │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_7 (Dense)     │ (None, 1)         │      1,025 │ dropout_3[0][0]   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,901,985\u001b[0m (91.18 MB)\n","text/html":"
 Total params: 23,901,985 (91.18 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,099,201\u001b[0m (8.01 MB)\n","text/html":"
 Trainable params: 2,099,201 (8.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m21,802,784\u001b[0m (83.17 MB)\n","text/html":"
 Non-trainable params: 21,802,784 (83.17 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:04:07.315290Z","iopub.execute_input":"2024-06-06T18:04:07.315582Z","iopub.status.idle":"2024-06-06T18:08:20.589100Z","shell.execute_reply.started":"2024-06-06T18:04:07.315557Z","shell.execute_reply":"2024-06-06T18:08:20.588055Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m 1/37\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m29:27\u001b[0m 49s/step - accuracy: 0.4360 - loss: 0.9417","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717697108.467899 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.5022 - loss: 7.0226 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717697145.098045 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717697152.906089 110 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.61257, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 2s/step - accuracy: 0.5021 - loss: 7.0423 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.5013 - loss: 7.9507\nEpoch 2: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 180ms/step - accuracy: 0.5013 - loss: 7.9501 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.4990 - loss: 7.9874\nEpoch 3: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 182ms/step - accuracy: 0.4991 - loss: 7.9858 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.5063 - loss: 7.8703\nEpoch 4: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 184ms/step - accuracy: 0.5062 - loss: 7.8719 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.5096 - loss: 7.8180\nEpoch 5: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 184ms/step - accuracy: 0.5094 - loss: 7.8209 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.5074 - loss: 7.8525\nEpoch 6: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 6: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 185ms/step - accuracy: 0.5073 - loss: 7.8545 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.5006 - loss: 7.9623\nEpoch 7: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step - accuracy: 0.5006 - loss: 7.9614 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-04\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.5043 - loss: 7.9020\nEpoch 8: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 182ms/step - accuracy: 0.5043 - loss: 7.9027 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-04\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - accuracy: 0.5112 - loss: 7.7928\nEpoch 9: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 180ms/step - accuracy: 0.5110 - loss: 7.7964 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-04\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.5079 - loss: 7.8448\nEpoch 10: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 179ms/step - accuracy: 0.5078 - loss: 7.8470 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-04\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.5102 - loss: 7.8079\nEpoch 11: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n\nEpoch 11: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 180ms/step - accuracy: 0.5100 - loss: 7.8110 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-04\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - accuracy: 0.4974 - loss: 8.0126\nEpoch 12: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 179ms/step - accuracy: 0.4975 - loss: 8.0104 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-05\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.5044 - loss: 7.9009\nEpoch 13: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 179ms/step - accuracy: 0.5044 - loss: 7.9016 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-05\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.4961 - loss: 8.0338\nEpoch 14: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 180ms/step - accuracy: 0.4962 - loss: 8.0310 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-05\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.5044 - loss: 7.9004\nEpoch 15: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 180ms/step - accuracy: 0.5044 - loss: 7.9011 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-05\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.5096 - loss: 7.8180\nEpoch 16: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n\nEpoch 16: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step - accuracy: 0.5094 - loss: 7.8209 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-05\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.4970 - loss: 8.0189\nEpoch 17: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 181ms/step - accuracy: 0.4972 - loss: 8.0165 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-06\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.5042 - loss: 7.9041\nEpoch 18: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 181ms/step - accuracy: 0.5042 - loss: 7.9048 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-06\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.4995 - loss: 7.9796\nEpoch 19: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 182ms/step - accuracy: 0.4996 - loss: 7.9783 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-06\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.4975 - loss: 8.0108\nEpoch 20: val_accuracy did not improve from 0.61257\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step - accuracy: 0.4976 - loss: 8.0087 - val_accuracy: 0.6126 - val_loss: 6.1766 - learning_rate: 1.0000e-06\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:08:20.590838Z","iopub.execute_input":"2024-06-06T18:08:20.591123Z","iopub.status.idle":"2024-06-06T18:08:20.867932Z","shell.execute_reply.started":"2024-06-06T18:08:20.591098Z","shell.execute_reply":"2024-06-06T18:08:20.866941Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:08:20.870372Z","iopub.execute_input":"2024-06-06T18:08:20.870754Z","iopub.status.idle":"2024-06-06T18:08:21.132608Z","shell.execute_reply.started":"2024-06-06T18:08:20.870721Z","shell.execute_reply":"2024-06-06T18:08:21.131664Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:08:21.133841Z","iopub.execute_input":"2024-06-06T18:08:21.134122Z","iopub.status.idle":"2024-06-06T18:09:03.722689Z","shell.execute_reply.started":"2024-06-06T18:08:21.134097Z","shell.execute_reply":"2024-06-06T18:09:03.721762Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.8386 - loss: 2.5725 \nTest Loss: 7.161477088928223\nTest Accuracy: 0.5507900714874268\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### MobileNet","metadata":{}},{"cell_type":"code","source":"from keras.applications import MobileNet\nfrom keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n\n# Loading MobileNet model\nmobilenet_model = MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=mobilenet_model.input, outputs=mobilenet_model.get_layer('conv_pw_13_relu').output)\n\n# Freezing convolutional layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Adding dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\nx = Dropout(rate=0.5)(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# Binding model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\n# Setting up callbacks\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1)\n\nmodel.summary() # model summary\n","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:09:49.748112Z","iopub.execute_input":"2024-06-06T18:09:49.748712Z","iopub.status.idle":"2024-06-06T18:09:50.598447Z","shell.execute_reply.started":"2024-06-06T18:09:49.748678Z","shell.execute_reply":"2024-06-06T18:09:50.597567Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n\u001b[1m17225924/17225924\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_19\"\u001b[0m\n","text/html":"
Model: \"functional_19\"\n
\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┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_4 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m864\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_2 (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m16,384\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_4 (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m32,768\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m2,304\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m65,536\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_6 (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m2,304\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m131,072\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,144\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,144\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,144\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,144\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,144\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_12 (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,608\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m524,288\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m9,216\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,048,576\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13_relu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_4 (InputLayer)      │ (None, 224, 224, 3)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1 (Conv2D)                  │ (None, 112, 112, 32)   │           864 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1_bn (BatchNormalization)   │ (None, 112, 112, 32)   │           128 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv1_relu (ReLU)               │ (None, 112, 112, 32)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1 (DepthwiseConv2D)     │ (None, 112, 112, 32)   │           288 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1_bn                    │ (None, 112, 112, 32)   │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_1_relu (ReLU)           │ (None, 112, 112, 32)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1 (Conv2D)              │ (None, 112, 112, 64)   │         2,048 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1_bn                    │ (None, 112, 112, 64)   │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_1_relu (ReLU)           │ (None, 112, 112, 64)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_2 (ZeroPadding2D)      │ (None, 113, 113, 64)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2 (DepthwiseConv2D)     │ (None, 56, 56, 64)     │           576 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2_bn                    │ (None, 56, 56, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_2_relu (ReLU)           │ (None, 56, 56, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2 (Conv2D)              │ (None, 56, 56, 128)    │         8,192 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2_bn                    │ (None, 56, 56, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_2_relu (ReLU)           │ (None, 56, 56, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3 (DepthwiseConv2D)     │ (None, 56, 56, 128)    │         1,152 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3_bn                    │ (None, 56, 56, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_3_relu (ReLU)           │ (None, 56, 56, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3 (Conv2D)              │ (None, 56, 56, 128)    │        16,384 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3_bn                    │ (None, 56, 56, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_3_relu (ReLU)           │ (None, 56, 56, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_4 (ZeroPadding2D)      │ (None, 57, 57, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4 (DepthwiseConv2D)     │ (None, 28, 28, 128)    │         1,152 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4_bn                    │ (None, 28, 28, 128)    │           512 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_4_relu (ReLU)           │ (None, 28, 28, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4 (Conv2D)              │ (None, 28, 28, 256)    │        32,768 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4_bn                    │ (None, 28, 28, 256)    │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_4_relu (ReLU)           │ (None, 28, 28, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5 (DepthwiseConv2D)     │ (None, 28, 28, 256)    │         2,304 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5_bn                    │ (None, 28, 28, 256)    │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_5_relu (ReLU)           │ (None, 28, 28, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5 (Conv2D)              │ (None, 28, 28, 256)    │        65,536 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5_bn                    │ (None, 28, 28, 256)    │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_5_relu (ReLU)           │ (None, 28, 28, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_6 (ZeroPadding2D)      │ (None, 29, 29, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6 (DepthwiseConv2D)     │ (None, 14, 14, 256)    │         2,304 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6_bn                    │ (None, 14, 14, 256)    │         1,024 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_6_relu (ReLU)           │ (None, 14, 14, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6 (Conv2D)              │ (None, 14, 14, 512)    │       131,072 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_6_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7 (DepthwiseConv2D)     │ (None, 14, 14, 512)    │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_7_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7 (Conv2D)              │ (None, 14, 14, 512)    │       262,144 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_7_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8 (DepthwiseConv2D)     │ (None, 14, 14, 512)    │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_8_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8 (Conv2D)              │ (None, 14, 14, 512)    │       262,144 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_8_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9 (DepthwiseConv2D)     │ (None, 14, 14, 512)    │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_9_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9 (Conv2D)              │ (None, 14, 14, 512)    │       262,144 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9_bn                    │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_9_relu (ReLU)           │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10 (DepthwiseConv2D)    │ (None, 14, 14, 512)    │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10_bn                   │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_10_relu (ReLU)          │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10 (Conv2D)             │ (None, 14, 14, 512)    │       262,144 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10_bn                   │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_10_relu (ReLU)          │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11 (DepthwiseConv2D)    │ (None, 14, 14, 512)    │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11_bn                   │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_11_relu (ReLU)          │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11 (Conv2D)             │ (None, 14, 14, 512)    │       262,144 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11_bn                   │ (None, 14, 14, 512)    │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_11_relu (ReLU)          │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pad_12 (ZeroPadding2D)     │ (None, 15, 15, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12 (DepthwiseConv2D)    │ (None, 7, 7, 512)      │         4,608 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12_bn                   │ (None, 7, 7, 512)      │         2,048 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_12_relu (ReLU)          │ (None, 7, 7, 512)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12 (Conv2D)             │ (None, 7, 7, 1024)     │       524,288 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12_bn                   │ (None, 7, 7, 1024)     │         4,096 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_12_relu (ReLU)          │ (None, 7, 7, 1024)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13 (DepthwiseConv2D)    │ (None, 7, 7, 1024)     │         9,216 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13_bn                   │ (None, 7, 7, 1024)     │         4,096 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_dw_13_relu (ReLU)          │ (None, 7, 7, 1024)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13 (Conv2D)             │ (None, 7, 7, 1024)     │     1,048,576 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13_bn                   │ (None, 7, 7, 1024)     │         4,096 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv_pw_13_relu (ReLU)          │ (None, 7, 7, 1024)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_3      │ (None, 1024)           │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_8 (Dense)                 │ (None, 1024)           │     1,049,600 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (Dropout)             │ (None, 1024)           │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (Dense)                 │ (None, 1)              │         1,025 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,279,489\u001b[0m (16.32 MB)\n","text/html":"
 Total params: 4,279,489 (16.32 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,050,625\u001b[0m (4.01 MB)\n","text/html":"
 Trainable params: 1,050,625 (4.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,228,864\u001b[0m (12.32 MB)\n","text/html":"
 Non-trainable params: 3,228,864 (12.32 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=20, batch_size=250,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:09:54.552844Z","iopub.execute_input":"2024-06-06T18:09:54.553550Z","iopub.status.idle":"2024-06-06T18:12:29.415195Z","shell.execute_reply.started":"2024-06-06T18:09:54.553513Z","shell.execute_reply":"2024-06-06T18:12:29.414146Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m 2/37\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 128ms/step - accuracy: 0.5090 - loss: 1.9705 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717697431.395330 109 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 538ms/step - accuracy: 0.5546 - loss: 3.0810","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717697450.806287 108 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717697454.238346 111 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.83639, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 747ms/step - accuracy: 0.5569 - loss: 3.0477 - val_accuracy: 0.8364 - val_loss: 0.3741 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.8649 - loss: 0.3278\nEpoch 2: val_accuracy improved from 0.83639 to 0.90576, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 127ms/step - accuracy: 0.8653 - loss: 0.3270 - val_accuracy: 0.9058 - val_loss: 0.2501 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9375 - loss: 0.1900\nEpoch 3: val_accuracy improved from 0.90576 to 0.94634, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 127ms/step - accuracy: 0.9378 - loss: 0.1894 - val_accuracy: 0.9463 - val_loss: 0.1701 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9770 - loss: 0.1019\nEpoch 4: val_accuracy improved from 0.94634 to 0.95550, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 128ms/step - accuracy: 0.9771 - loss: 0.1016 - val_accuracy: 0.9555 - val_loss: 0.1322 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9882 - loss: 0.0624\nEpoch 5: val_accuracy improved from 0.95550 to 0.96335, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 131ms/step - accuracy: 0.9882 - loss: 0.0623 - val_accuracy: 0.9634 - val_loss: 0.1154 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9908 - loss: 0.0481\nEpoch 6: val_accuracy improved from 0.96335 to 0.97251, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 128ms/step - accuracy: 0.9908 - loss: 0.0480 - val_accuracy: 0.9725 - val_loss: 0.0853 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9948 - loss: 0.0309\nEpoch 7: val_accuracy improved from 0.97251 to 0.97644, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 129ms/step - accuracy: 0.9948 - loss: 0.0308 - val_accuracy: 0.9764 - val_loss: 0.0752 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9976 - loss: 0.0217\nEpoch 8: val_accuracy improved from 0.97644 to 0.97775, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 128ms/step - accuracy: 0.9976 - loss: 0.0217 - val_accuracy: 0.9777 - val_loss: 0.0612 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9978 - loss: 0.0170\nEpoch 9: val_accuracy improved from 0.97775 to 0.98168, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 128ms/step - accuracy: 0.9978 - loss: 0.0170 - val_accuracy: 0.9817 - val_loss: 0.0529 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9977 - loss: 0.0141\nEpoch 10: val_accuracy improved from 0.98168 to 0.98560, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 126ms/step - accuracy: 0.9977 - loss: 0.0141 - val_accuracy: 0.9856 - val_loss: 0.0473 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9988 - loss: 0.0115\nEpoch 11: val_accuracy improved from 0.98560 to 0.99084, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 128ms/step - accuracy: 0.9988 - loss: 0.0114 - val_accuracy: 0.9908 - val_loss: 0.0418 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9992 - loss: 0.0092\nEpoch 12: val_accuracy did not improve from 0.99084\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 120ms/step - accuracy: 0.9992 - loss: 0.0092 - val_accuracy: 0.9908 - val_loss: 0.0376 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.9992 - loss: 0.0077\nEpoch 13: val_accuracy did not improve from 0.99084\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 0.9992 - loss: 0.0077 - val_accuracy: 0.9895 - val_loss: 0.0460 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.9987 - loss: 0.0074\nEpoch 14: val_accuracy improved from 0.99084 to 0.99215, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 130ms/step - accuracy: 0.9987 - loss: 0.0074 - val_accuracy: 0.9921 - val_loss: 0.0346 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9996 - loss: 0.0070\nEpoch 15: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 0.9996 - loss: 0.0069 - val_accuracy: 0.9921 - val_loss: 0.0346 - learning_rate: 0.0010\nEpoch 16/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9998 - loss: 0.0052\nEpoch 16: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 120ms/step - accuracy: 0.9998 - loss: 0.0052 - val_accuracy: 0.9895 - val_loss: 0.0393 - learning_rate: 0.0010\nEpoch 17/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 1.0000 - loss: 0.0035\nEpoch 17: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 1.0000 - loss: 0.0035 - val_accuracy: 0.9895 - val_loss: 0.0377 - learning_rate: 0.0010\nEpoch 18/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.9997 - loss: 0.0038\nEpoch 18: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 0.9997 - loss: 0.0038 - val_accuracy: 0.9908 - val_loss: 0.0356 - learning_rate: 0.0010\nEpoch 19/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.9999 - loss: 0.0036\nEpoch 19: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 0.9999 - loss: 0.0036 - val_accuracy: 0.9921 - val_loss: 0.0316 - learning_rate: 0.0010\nEpoch 20/20\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.9997 - loss: 0.0024\nEpoch 20: val_accuracy did not improve from 0.99215\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 118ms/step - accuracy: 0.9997 - loss: 0.0024 - val_accuracy: 0.9921 - val_loss: 0.0336 - learning_rate: 0.0010\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:12:29.427656Z","iopub.execute_input":"2024-06-06T18:12:29.427924Z","iopub.status.idle":"2024-06-06T18:12:29.643912Z","shell.execute_reply.started":"2024-06-06T18:12:29.427901Z","shell.execute_reply":"2024-06-06T18:12:29.643046Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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b3PHnvsMezZswd//PEHvvvuO9xxxx1QqVSYOnWqTa+lpcwTIRZdqUKVwShzbYiIiFyTrE1gBw8exOjRo6X3KSkpAICkpCSsXr0aOTk59UZwFRUV4dNPP8Xrr7/e4DnPnTuHqVOnoqCgAJ06dcKIESOwb98+dOrUyXYX0gp+Hm5QKgCjAC6XViLY113uKhEREbkchRCCy5JfRa/XQ6fToaioCL6+vlY///XP7UB+SSW2PnwTrgmz/vmJiIhcUWv+fjtlHyBnVzsZIvsBERERyYEBSAbmyRA5FJ6IiEgeDEAykIbC8wkQERGRLBiAZBDIJ0BERESyYgCSgXkoPFeEJyIikgcDkAykBVHZBEZERCQLBiAZSAui8gkQERGRLBiAZGBuAisoYR8gIiIiOTAAyaB2GDyfABEREcmBAUgGQTUTIRaXV6Oi2iBzbYiIiFwPA5AMfD3UUCsVANgPiIiISA4MQDJQKBS1zWAcCUZERGR3DEAyYT8gIiIi+TAAySRImgyRI8GIiIjsjQFIJmwCIyIikg8DkEykBVHZBEZERGR3DEAykRZE5WSIREREdscAJBMuiEpERCQfBiCZBHJBVCIiItkwAMmktg8Qm8CIiIjsjQFIJoE1y2Fc4hMgIiIiu2MAkklAzROg0koDyqu4HhgREZE9MQDJxEerhkZl+ufnUHgiIiL7YgCSieV6YOwHREREZE8MQDLiZIhERETyYACSEZfDICIikgcDkIy4ICoREZE8GIBkFMgnQERERLJgAJKReSg8Z4MmIiKyLwYgGQV5sQmMiIhIDgxAMpI6QXMUGBERkV0xAMlIGgbPJjAiIiK7YgCSkXk9MC6ISkREZF8MQDIyPwEqrzKirLJa5toQERG5DgYgGXlqVNCqa9YDYzMYERGR3cgagPbu3Yvx48cjPDwcCoUCmzZtarL87t27oVAo6m25ubkW5ZYtW4Zu3brB3d0dMTExOHDggA2vou0UCoU0GSI7QhMREdmPrAGotLQUUVFRWLZsWauOO378OHJycqQtODhY+uzjjz9GSkoKUlNTcfjwYURFRSE+Ph4XLlywdvWtorYjNPsBERER2Ytazi9PTExEYmJiq48LDg6Gn59fg5+9+uqruP/++5GcnAwAWLFiBTZv3oxVq1bhqaeeak91bYLrgREREdmfU/YBGjx4MMLCwnDrrbfi22+/lfZXVlbi0KFDiIuLk/YplUrExcUhMzNTjqo2q3YkGAMQERGRvThVAAoLC8OKFSvw6aef4tNPP0VERARGjRqFw4cPAwDy8/NhMBgQEhJicVxISEi9fkJ1VVRUQK/XW2z2wiYwIiIi+5O1Cay1+vbti759+0rvhw8fjlOnTuG1117Df//73zafNy0tDYsWLbJGFVvNvCDqJT4BIiIishunegLUkGHDhuHkyZMAgKCgIKhUKuTl5VmUycvLQ2hoaKPnmDdvHoqKiqQtOzvbpnWuy9wHKJ8BiIiIyG6cPgBlZWUhLCwMAKDRaBAdHY2MjAzpc6PRiIyMDMTGxjZ6Dq1WC19fX4vNXszD4LkgKhERkf3I2gRWUlIiPb0BgNOnTyMrKwsBAQHo2rUr5s2bhz///BNr164FACxZsgTdu3fHgAEDUF5ejnfffRdfffUVtm/fLp0jJSUFSUlJuP766zFs2DAsWbIEpaWl0qgwR8NRYERERPYnawA6ePAgRo8eLb1PSUkBACQlJWH16tXIycnB2bNnpc8rKyvx6KOP4s8//4SnpycGDRqEnTt3Wpxj8uTJuHjxIhYsWIDc3FwMHjwY6enp9TpGOwqpE3RpJYQQUCgUMteIiIio41MIIYTclXA0er0eOp0ORUVFNm8Ou1JpwDUL0gEAPy0cCx93N5t+HxERUUfVmr/fTt8HyNl5aFTw1KgAsBmMiIjIXhiAHEDdZjAiIiKyPQYgBxBgng2akyESERHZBQOQAwjiZIhERER2xQDkAKSh8AxAREREdsEA5AACvc1NYAxARERE9sAA5AACpSdA7ANERERkDwxADsA8Cox9gIiIiOyDAcgBSAuisgmMiIjILhiAHECQN4fBExER2RMDkAOo2wTGlUmIiIhsjwHIAZibwKqNAvor1TLXhoiIqONjAHIAWrUKPlo1AI4EIyIisgcGIAcRwPXAiIiI7IYByEFIcwFxJBgREZHNMQA5CGlBVDaBERER2RwDkIMIMo8E4xMgIiIim2MAchBcEJWIiMh+GIAchHlB1HxOhkhERGRzDEAOIojrgREREdkNA5CDCOAoMCIiIrthAHIQgdIoMAYgIiIiW2MAchDm9cAul1XCaOR6YERERLbEAOQg/D1NAchgFCi6UiVzbYiIiDo2BiAHoVEr4evO9cCIiIjsgQHIgQTVDIVnR2giIiLbYgByIJwMkYiIyD4YgByIuSN0ASdDJCIisikGIAcSwKHwREREdsEA5ECCvDkZIhERkT0wADmQQC8uh0FERGQPDEAOJIALohIREdkFA5ADCeITICIiIrtgAHIgAd4cBk9ERGQPDEAOxLwg6uWyShi4HhgREZHNMAA5EH9PNwCAEKYQRERERLYhawDau3cvxo8fj/DwcCgUCmzatKnJ8p999hluvfVWdOrUCb6+voiNjcW2bdssyixcuBAKhcJi69evnw2vwnrUKqUUgjgUnoiIyHZkDUClpaWIiorCsmXLWlR+7969uPXWW7FlyxYcOnQIo0ePxvjx43HkyBGLcgMGDEBOTo60ffPNN7aovk3ULofBkWBERES2opbzyxMTE5GYmNji8kuWLLF4/+9//xuff/45/ve//2HIkCHSfrVajdDQUGtV064CvbU4dbGUT4CIiIhsyKn7ABmNRhQXFyMgIMBi/4kTJxAeHo4ePXpg2rRpOHv2bJPnqaiogF6vt9jkYp4NmkPhiYiIbMepA9DLL7+MkpISTJo0SdoXExOD1atXIz09HcuXL8fp06dx0003obi4uNHzpKWlQafTSVtERIQ9qt8gqQmMkyESERHZjNMGoA8//BCLFi3C+vXrERwcLO1PTEzEPffcg0GDBiE+Ph5btmxBYWEh1q9f3+i55s2bh6KiImnLzs62xyU0KJALohIREdmcrH2A2uqjjz7CzJkzsWHDBsTFxTVZ1s/PD3369MHJkycbLaPVaqHVaq1dzTYJ5IKoRERENud0T4DWrVuH5ORkrFu3DuPGjWu2fElJCU6dOoWwsDA71K79zE+A2AeIiIjIdmR9AlRSUmLxZOb06dPIyspCQEAAunbtinnz5uHPP//E2rVrAZiavZKSkvD6668jJiYGubm5AAAPDw/odDoAwGOPPYbx48cjMjIS58+fR2pqKlQqFaZOnWr/C2wDcx+gfA6DJyIishlZnwAdPHgQQ4YMkYawp6SkYMiQIViwYAEAICcnx2IE19tvv43q6mrMnj0bYWFh0vbwww9LZc6dO4epU6eib9++mDRpEgIDA7Fv3z506tTJvhfXRkFsAiMiIrI5hRCCi05dRa/XQ6fToaioCL6+vnb97oKSCkQ/txMAcOL5RLipnK6VkoiISBat+fvNv64Oxs9TA6XC9Poy+wERERHZBAOQg1EpFfD3NC+HwQBERERkCwxADohD4YmIiGyLAcgB1U6GyJFgREREtsAA5IAC+ASIiIjIphiAHFCQFxdEJSIisiUGIAcUwCYwIiIim2IAckDmTtD5bAIjIiKyCQYgBxTIJjAiIiKbYgByQIHeNU1gJWwCIyIisgUGIAdkXhCVEyESERHZBgOQAzIviFpcXo2KaoPMtSEiIup4GIAckK+7G9Q1C4JdLq2SuTZEREQdDwOQA1IqFfD3Mo8EYz8gIiIia2MAclAcCUZERGQ7DEAOSloQlZMhEhERWR0DkIOSFkTlZIhERERWxwDkoDgUnoiIyHYYgBxUkLQiPJvAiIiIrI0ByEGZF0RlJ2giIiLrYwByUFwQlYiIyHYYgByUuQmMT4CIiIisjwHIQQV4cUFUIiIiW2EAclDmJrDSSgPKq7geGBERkTUxADkoH60abirTemAcCk9ERGRdDEAOSqFQ1JkMkc1gRERE1sQA5MA4GSIREZFtMAA5MGk9MA6FJyIisioGIAdWuyI8m8CIiIisiQHIgQV6c0FUIiIiW2AAcmDsA0RERGQbDEAOjAuiEhER2QYDkAML5IKoRERENsEA5MACuCAqERGRTTAAObAg80SIHAVGRERkVbIGoL1792L8+PEIDw+HQqHApk2bmj1m9+7duO6666DVatGrVy+sXr26Xplly5ahW7ducHd3R0xMDA4cOGD9ytuB+QlQeZURZZXVMteGiIio45A1AJWWliIqKgrLli1rUfnTp09j3LhxGD16NLKysvDII49g5syZ2LZtm1Tm448/RkpKClJTU3H48GFERUUhPj4eFy5csNVl2IyXRgWt2nSLOBSeiIjIehRCCCF3JQDT2lcbN27ExIkTGy3z5JNPYvPmzfj555+lfVOmTEFhYSHS09MBADExMRg6dCiWLl0KADAajYiIiMDcuXPx1FNPtaguer0eOp0ORUVF8PX1bftFWcHwtAycLyrHptk3YnCEn6x1ISIicmSt+fvtVH2AMjMzERcXZ7EvPj4emZmZAIDKykocOnTIooxSqURcXJxUpiEVFRXQ6/UWm6OonQyR/YCIiIisxakCUG5uLkJCQiz2hYSEQK/X48qVK8jPz4fBYGiwTG5ubqPnTUtLg06nk7aIiAib1L8tOBkiERGR9TlVALKVefPmoaioSNqys7PlrpKEC6ISERFZn1ruCrRGaGgo8vLyLPbl5eXB19cXHh4eUKlUUKlUDZYJDQ1t9LxarRZardYmdW6vIG/zZIhsAiMiIrIWp3oCFBsbi4yMDIt9O3bsQGxsLABAo9EgOjraoozRaERGRoZUxtlITWB8AkRERGQ1sgagkpISZGVlISsrC4BpmHtWVhbOnj0LwNQ0NX36dKn83//+d/z+++944okn8Ouvv+I///kP1q9fj3/84x9SmZSUFLzzzjtYs2YNjh07hgcffBClpaVITk6267VZS2BNAMpnHyAiIiKraVMTWHZ2NhQKBbp06QIAOHDgAD788EP0798fs2bNavF5Dh48iNGjR0vvU1JSAABJSUlYvXo1cnJypDAEAN27d8fmzZvxj3/8A6+//jq6dOmCd999F/Hx8VKZyZMn4+LFi1iwYAFyc3MxePBgpKen1+sY7SzMfYDYBEZERGQ9bZoH6KabbsKsWbNw7733Ijc3F3379sWAAQNw4sQJzJ07FwsWLLBFXe3GkeYB+iG7EBOWfYswnTsy542RtS5ERESOzObzAP38888YNmwYAGD9+vW49tpr8d133+GDDz5ocGkKaru6w+AdZM5KIiIip9emAFRVVSWNmtq5cyduv/12AEC/fv2Qk5NjvdqR1ARWWW1ESQXXAyMiIrKGNgWgAQMGYMWKFfj666+xY8cOJCQkAADOnz+PwMBAq1bQ1Xlq1PBwUwEALrEjNBERkVW0KQC9+OKLeOuttzBq1ChMnToVUVFRAIAvvvhCahoj6zE/BcrnUHgiIiKraNMosFGjRiE/Px96vR7+/v7S/lmzZsHT09NqlSOTQG8tzl2+widAREREVtKmJ0BXrlxBRUWFFH7OnDmDJUuW4Pjx4wgODrZqBal2LiAuiEpERGQdbQpAEyZMwNq1awEAhYWFiImJwSuvvIKJEydi+fLlVq0g1QlAfAJERERkFW0KQIcPH8ZNN90EAPjkk08QEhKCM2fOYO3atXjjjTesWkECArggKhERkVW1KQCVlZXBx8cHALB9+3bceeedUCqVuOGGG3DmzBmrVpCAIC/TlAMFnA2aiIjIKtoUgHr16oVNmzYhOzsb27Ztw9ixYwEAFy5ckH3m5I7IPBkiO0ETERFZR5sC0IIFC/DYY4+hW7duGDZsmLTS+vbt2zFkyBCrVpA4DJ6IiMja2jQM/u6778aIESOQk5MjzQEEAGPGjMEdd9xhtcqRSWBNExgXRCUiIrKONgUgAAgNDUVoaCjOnTsHAOjSpQsnQbSR2hXhTeuBKRQKmWtERETk3NrUBGY0GvHMM89Ap9MhMjISkZGR8PPzw7PPPguj0WjtOro8cx+gKoOAvpzrgREREbVXm54APf3001i5ciVeeOEF3HjjjQCAb775BgsXLkR5eTmef/55q1bS1bm7qeCtVaOkohoFJRXQebjJXSUiIiKn1qYAtGbNGrz77rvSKvAAMGjQIHTu3BkPPfQQA5ANBHprTAGotBI9OsldGyIiIufWpiawS5cuoV+/fvX29+vXD5cuXWp3pai+AC9OhkhERGQtbQpAUVFRWLp0ab39S5cuxaBBg9pdKaovkJMhEhERWU2bmsAWL16McePGYefOndIcQJmZmcjOzsaWLVusWkEyMa8HdolPgIiIiNqtTU+ARo4cid9++w133HEHCgsLUVhYiDvvvBNHjx7Ff//7X2vXkVA7FJ4LohIREbVfm+cBCg8Pr9fZ+YcffsDKlSvx9ttvt7tiZCmAK8ITERFZTZueAJH9BXnX9AEqYR8gIiKi9mIAchJcEJWIiMh6GICcBBdEJSIisp5W9QG68847m/y8sLCwPXWhJpibwC6XVcJoFFAquR4YERFRW7UqAOl0umY/nz59ersqRA3z9zQ9ATIYBYquVMG/pkmMiIiIWq9VAei9996zVT2oGRq1Er7uaujLq1FQWsEARERE1A7sA+REAqWRYOwHRERE1B4MQE4kkHMBERERWQUDkBPhZIhERETWwQDkRAI5GSIREZFVMAA5kUBOhkhERGQVDEBORFoQlZ2giYiI2oUByImY+wDlswmMiIioXRiAnIh5Nmg2gREREbWPQwSgZcuWoVu3bnB3d0dMTAwOHDjQaNlRo0ZBoVDU28aNGyeVmTFjRr3PExIS7HEpNiU1gTEAERERtUurZoK2hY8//hgpKSlYsWIFYmJisGTJEsTHx+P48eMIDg6uV/6zzz5DZWVtACgoKEBUVBTuuecei3IJCQkWM1drtVrbXYSdmJvALpdVwmAUUHE9MCIiojaR/QnQq6++ivvvvx/Jycno378/VqxYAU9PT6xatarB8gEBAQgNDZW2HTt2wNPTs14A0mq1FuX8/f3tcTk2FVCzHpgQphBEREREbSNrAKqsrMShQ4cQFxcn7VMqlYiLi0NmZmaLzrFy5UpMmTIFXl5eFvt3796N4OBg9O3bFw8++CAKCgoaPUdFRQX0er3F5ojUKiX8PN0AsB8QERFRe8gagPLz82EwGBASEmKxPyQkBLm5uc0ef+DAAfz888+YOXOmxf6EhASsXbsWGRkZePHFF7Fnzx4kJibCYDA0eJ60tDTodDppi4iIaPtF2VggR4IRERG1m+x9gNpj5cqVGDhwIIYNG2axf8qUKdLrgQMHYtCgQejZsyd2796NMWPG1DvPvHnzkJKSIr3X6/UOG4ICvbQ4dbGUT4CIiIjaQdYnQEFBQVCpVMjLy7PYn5eXh9DQ0CaPLS0txUcffYT77ruv2e/p0aMHgoKCcPLkyQY/12q18PX1tdgcFSdDJCIiaj9ZA5BGo0F0dDQyMjKkfUajERkZGYiNjW3y2A0bNqCiogJ/+9vfmv2ec+fOoaCgAGFhYe2us9ykBVHZBEZERNRmso8CS0lJwTvvvIM1a9bg2LFjePDBB1FaWork5GQAwPTp0zFv3rx6x61cuRITJ05EYGCgxf6SkhI8/vjj2LdvH/744w9kZGRgwoQJ6NWrF+Lj4+1yTbYkLYjKJjAiIqI2k70P0OTJk3Hx4kUsWLAAubm5GDx4MNLT06WO0WfPnoVSaZnTjh8/jm+++Qbbt2+vdz6VSoUff/wRa9asQWFhIcLDwzF27Fg8++yzHWIuoCA2gREREbWbQggh5K6Eo9Hr9dDpdCgqKnK4/kBf/ngecz48gmHdArD+7003ExIREbmS1vz9lr0JjFon0Mv0FCu/lH2AiIiI2ooByMmYR4FxGDwREVHbMQA5GfNEiIVlVagyGGWuDRERkXNiAHIyfp4aKGrWQOV6YERERG3DAORkVEqFtCgqR4IRERG1DQOQE6qdDJEBiIiIqC0YgJyQtBwGR4IRERG1CQOQE5Jmg+YTICIiojZhAHJC5pFgHApPRETUNgxATsg8GSKbwIiIiNqGAcgJBXA9MCIionZhAHJCQeZRYGwCIyIiahMGICcUwD5ARERE7cIA5ITMo8DyS9gHiIiIqC0YgJyQeRRYcXk1KqoNMteGiIjI+TAAOSGdhxtUStOCYJdLq2SuDRERkfNhAHJCSqUC/jXrgbEZjIiIqPUYgJxUkDc7QhMREbUVA5CT4npgREREbccA5KQCvLgeGBERUVsxADmpQE6GSERE1GYMQE5KWhCVT4CIiIhajQHISZknQ2QfICIiotZjAHJS5uUw8vkEiIiIqNUYgJwUh8ETERG1HQOQkzI/ASrgRIhEREStxgDkpMx9gEorDSiv4npgRERErcEA5KR83dVwU5nWA+NQeCIiotZhAHJSCoVCagbjUHgiIqLWYQByYoE1s0Hncyg8ERFRqzAAOTHzemB8AkRERNQ6DEBOrHY5DD4BIiIiag0GICfGBVGJiIjahgHIiZmbwDgKjIiIqHUYgJxYICdDJCIiahOHCEDLli1Dt27d4O7ujpiYGBw4cKDRsqtXr4ZCobDY3N3dLcoIIbBgwQKEhYXBw8MDcXFxOHHihK0vw+7MkyFyOQwiIqLWkT0Affzxx0hJSUFqaioOHz6MqKgoxMfH48KFC40e4+vri5ycHGk7c+aMxeeLFy/GG2+8gRUrVmD//v3w8vJCfHw8ysvLbX05dmVuAuOCqERERK0jewB69dVXcf/99yM5ORn9+/fHihUr4OnpiVWrVjV6jEKhQGhoqLSFhIRInwkhsGTJEvzrX//ChAkTMGjQIKxduxbnz5/Hpk2b7HBF9mNuAuMTICIiotaRNQBVVlbi0KFDiIuLk/YplUrExcUhMzOz0eNKSkoQGRmJiIgITJgwAUePHpU+O336NHJzcy3OqdPpEBMT0+Q5nZG5CexKlQFlldUy14aIiMh5yBqA8vPzYTAYLJ7gAEBISAhyc3MbPKZv375YtWoVPv/8c7z//vswGo0YPnw4zp07BwDSca05Z0VFBfR6vcXmDLw0KmjUplvIofBEREQtJ3sTWGvFxsZi+vTpGDx4MEaOHInPPvsMnTp1wltvvdXmc6alpUGn00lbRESEFWtsOwqFAkFeHApPRETUWrIGoKCgIKhUKuTl5Vnsz8vLQ2hoaIvO4ebmhiFDhuDkyZMAIB3XmnPOmzcPRUVF0padnd3aS5FNgDeHwhMREbWWrAFIo9EgOjoaGRkZ0j6j0YiMjAzExsa26BwGgwE//fQTwsLCAADdu3dHaGioxTn1ej3279/f6Dm1Wi18fX0tNmdhXhCVT4CIiIhaTi13BVJSUpCUlITrr78ew4YNw5IlS1BaWork5GQAwPTp09G5c2ekpaUBAJ555hnccMMN6NWrFwoLC/HSSy/hzJkzmDlzJgBTs9AjjzyC5557Dr1790b37t0xf/58hIeHY+LEiXJdps3UTobIAERERNRSsgegyZMn4+LFi1iwYAFyc3MxePBgpKenS52Yz549C6Wy9kHV5cuXcf/99yM3Nxf+/v6Ijo7Gd999h/79+0tlnnjiCZSWlmLWrFkoLCzEiBEjkJ6eXm/CxI5AWhGeC6ISERG1mEIIIeSuhKPR6/XQ6XQoKipy+Oaw5btP4cX0X3HnkM54dfJguatDREQkm9b8/Xa6UWBkiQuiEhERtR4DkJMLkgIQm8CIiIhaigHIyQXUjAK7xE7QRERELcYA5OTMo8DySyvB7lxEREQtwwDk5Mx9gCqrjSip4HpgRERELcEA5OQ8NWp4uKkAcFV4IiKilmIA6gACzM1g7AdERETUIgxAHUCQNBkiAxAREVFLMAB1AAFeXBCViIioNRiAOoBAby6ISkRE1BoMQB2ANBs0+wARERG1CANQB2CeC4gLohIREbUMA1AHEOjFJjAiIqLWYADqAAK8OQyeiIioNRiAOoAg83pgbAIjIiJqEQYgeys4ZfVTBtSZB4jrgRERETWPAciejv0PWDYM+PZ1q57W3Am6yiCgL+d6YERERM1hALKnvKOAsRrYsQDYsxiw0tMadzcVvDSm9cA4GSIREVHzGIDsadRTwC3zTa93PQ989azVQpB5MkQuh0FERNQ8BiB7u/kxYOzzptdfvwJse9oqIYgLohIREbUcA5Achs8BbnvZ9HrfMmDzo4DR2K5TckFUIiKilmMAksuw+4Hb3wSgAA6uBP43FzAa2nw6aTJE9gEiIiJqFgOQnK6bDtzxFqBQAkfeBzb+HTC0bRSXeSg8Z4MmIiJqHgOQ3KImA3evApRq4Kf1wKf/B1S3PsSYh8IzABERETWPAcgRDLgDmPRfQKUBfvkcWD8dqG5dU1btivBsAiMiImoOA5Cj6HcbMHUdoHYHftsKrJsKVJa1+PBALw6DJyIiaikGIEfSKw6YtgFw8wROZQAfTgIqSlp0KIfBExERtRwDkKPpfjNw70ZA4wP88TXw/l1AeVGzhwXVTIR4uawSRiPXAyMiImoKA5Aj6noDMP1zwF0HZO8D1k4Erlxu8hB/LzcAgMEoUHSlyg6VJCIicl4MQI6qSzSQ9D/AIwA4fxhYMx4ozW+0uFatgo+7GgBHghERETWHAciRhUUBMzYDXsFA7k/A6r8AxXmNFjc3g3EkGBERUdMYgBxdSH8geQvgEw5cPAasvg0o+rPBoua5gL46fsGeNSQiInI6DEDOIKi3KQTpugIFJ4H3EoHLZ+oVuzu6CwDgrT2/482ME/auJRERkdNgAHIWAd1NIci/O1B4BnjvNqDglEWRKcO64omEvgCAV3b8xhBERETUCAYgZ+IXASRvBYL6APpzphB08TeLIg+N6mURgpZ+xRBERER0NQYgZ+MbBszYAgQPAEpyTX2C8o5aFHloVC88Hm8KQS9v/w3Ldp2Uo6ZEREQOyyEC0LJly9CtWze4u7sjJiYGBw4caLTsO++8g5tuugn+/v7w9/dHXFxcvfIzZsyAQqGw2BISEmx9Gfbj3QmY8aVplFjpRWD1OOB8lkWR2aNrQ9BL244zBBEREdUhewD6+OOPkZKSgtTUVBw+fBhRUVGIj4/HhQsNj2TavXs3pk6dil27diEzMxMREREYO3Ys/vzTcmRUQkICcnJypG3dunX2uBz78QwApn8BdL7eNEnimtuB7O8tiswe3QuPje0DgCGIiIioLoUQQtZ1E2JiYjB06FAsXboUAGA0GhEREYG5c+fiqaeeavZ4g8EAf39/LF26FNOnTwdgegJUWFiITZs2talOer0eOp0ORUVF8PX1bdM57KaiGPhgEnD2O0DjbVpLLHK4RZGlX53Ay9tNfYWeSOiLh0b1kqOmRERENtWav9+yPgGqrKzEoUOHEBcXJ+1TKpWIi4tDZmZmi85RVlaGqqoqBAQEWOzfvXs3goOD0bdvXzz44IMoKCho9BwVFRXQ6/UWm9PQ+gB/+wToMQqoLAH+eydw4B3AaJCKzLmlNx691fQkaHH6cfxnN58EERGRa5M1AOXn58NgMCAkJMRif0hICHJzc1t0jieffBLh4eEWISohIQFr165FRkYGXnzxRezZsweJiYkwGAwNniMtLQ06nU7aIiIi2n5RctB4AVM/BnrHA9VXgC2PAe/GATk/SkXmjumNlDohaPnuU42djYiIqMOTvQ9Qe7zwwgv46KOPsHHjRri7u0v7p0yZgttvvx0DBw7ExIkT8eWXX+L777/H7t27GzzPvHnzUFRUJG3Z2dl2ugIrcnMHpq4Dxr0CaH1N64e9PQrY9jRQUQIA+H91QtCL6b9ixR6GICIick2yBqCgoCCoVCrk5Vmub5WXl4fQ0NAmj3355ZfxwgsvYPv27Rg0aFCTZXv06IGgoCCcPNlw049Wq4Wvr6/F5pSUKmDoTGDO98CAOwBhADKXAv+5ATieDsAUgv4RZwpBL2xlCCIiItckawDSaDSIjo5GRkaGtM9oNCIjIwOxsbGNHrd48WI8++yzSE9Px/XXX9/s95w7dw4FBQUICwuzSr0dnk8ocM9qYNongF9XoCgbWDcZ+PheQH8eD8f1xiNxvQGYQtBbDEFERORiZG8CS0lJwTvvvIM1a9bg2LFjePDBB1FaWork5GQAwPTp0zFv3jyp/Isvvoj58+dj1apV6NatG3Jzc5Gbm4uSElMzT0lJCR5//HHs27cPf/zxBzIyMjBhwgT06tUL8fHxslyjbHrfCjy0H7jxEUChAo59ASwdBux/C4/c0lMKQWlbf8XbexmCiIjIdcgegCZPnoyXX34ZCxYswODBg5GVlYX09HSpY/TZs2eRk5MjlV++fDkqKytx9913IywsTNpefvllAIBKpcKPP/6I22+/HX369MF9992H6OhofP3119BqtbJco6w0nsCti4AH9gJdhgKVxcDWJ4B3x+CRAVfw8BhTCPr3ll/xzt7fZa4sERGRfcg+D5Ajcqp5gFrDaAQOvQfsXARUFAEKJXDDQ1gq7sHLu00TST592zW4/+YeMleUiIio9ZxmHiCyM6USGHofMOcAMOBOQBiBzKWY88s0LBliesr2/JZjePdrPgkiIqKOjQHIFfmEAve8B0z7FPCLBPTnMPHYo9gR/jZCUYDnNjMEERFRx8YA5Mp6xwEP7QNG/ANQqtH70m7s9XwSM1Tp+PfmowxBRETUYTEAuTqNJxC3sKaT9DBojGVY6LYWGzULsHHLFoYgIiLqkBiAyCRkAPB/24C/vAah9UWU8nd8ofkXsO2fWLP7qNy1IyIisioGIKqlVALX/x8Ucw5CXHs3VAqBmeqtiNs1Hjs/WyV37YiIiKyGAYjq8wmB4u6VENM+RaE2HJ0VBYj78R84s2wiUOiE66QRERFdhQGIGqXoHQfdoweRGZ6EKqFC5MVdEEsGQrx7K7D3ZSD3Z4DTSBERkRPiRIgN6LATIbaREALvbdyCa448h1jVL5Yf+nYB+sSbtu43A24e8lSSiIhcXmv+fjMANYABqD4hBFZ+cxrvb89ErPEQxiiP4Gb1UWhERW0htQfQY6QpDPWOB3Sd5aswERG5HAagdmIAalxuUTkWp/+Kz478CS0qcYv2V8zpfAr9S76DQv+nZeHQgUCfBNMWfp2pkzUREZGNMAC1EwNQ8w6fvYxF//sFP2QXAgAiAzzwwggVbqg+CMWJbUD2AQB1/tPy6gT0Hmvaet4CuPPflYiIrIsBqJ0YgFrGaBT47MifeDH9V1wsNjWF3dQ7CPP/0h99vCuAkzuB39KBkxlAhb72QKUbEDm85ulQPBDYU6YrICKijoQBqJ0YgFqnpKIay3adxMqvT6PSYIRKqcC9N0Tikbje8PPUAIYq4Gwm8Ns201ZwwvIEgb1rOlInAJ2vAzRe8lwIERE5NQagdmIAapszBaV4fvMxbP8lDwDg5+mGR2/tg6nDukKtqtP/p+BUTRhKB858CxirLU/k1cm0SKt/N8C/5qf5vW9nQKW21yUREZETYQBqJwag9vnmRD6e+fIofssrAQD0DfFB6vj+GN4rqH7h8iLg1C5TIDq5Eyi90PTJFSrAL6ImENUNR91N7z0DAYXC+hdFREQOjwGonRiA2q/aYMSHB87ile2/oehKFQAgfkAInr6tP7oGejZ+4JVCoPAMcPkP4HLNT/P7wrOAobLpL9Z4Wz49uvq1ponvJiIip8YA1E4MQNZzubQSS3b+hvf3n4XBKKBRKTHzpu54aHQveGtb2ZRlNAIluQ2Ho8tngOLzzZ8juD/Q9Qag63DTT7+INlwVERE5IgagdmIAsr7jucV45suj+PZkAQAg2EeLJxP64Y4hnaFUWqnJqqocKMquCUenr3qSdAaoKKp/jG+XmkB0g2lkWqdrOF8REZGTYgBqJwYg2xBCYMcveXhu8zGcvVQGAIiK8EPq+P64rqu/7StQnAdk7wfO7jONSsv5ARAGyzJaHdA1piYUxZomcHRzt33diIio3RiA2okByLYqqg1Y9c0fWPrVCZRWmgLInUM648nEfgjxtWPYqCgB/jxkCkNnM4Hs74GqUssyKg0QPsQUhrrGAhHDAM8A+9WRiIhajAGonRiA7OOCvhyLtx3HJ4fOAQA8NSrcMaQzhvcMwg09AhDorbVvhQzVQN5PtU+IzmQ2PCqt0zW1T4i63gD4deXIMyIiB8AA1E4MQPb1Q3YhFv7vKI6cLbTY3y/UBzf0CMTwnoGI6R4InaebfSsmhKkv0ZmaJ0Rn99WfxBEwzU3U9QbTkyLvENMTIs/A2o0TOxIR2QUDUDsxANmfEAK7j1/Ent8uIvNUAY7nFVt8rlAAA8J9MbxnEGJ7BGJo94DWjyKzhtL82idEZ/cBOVn1J3K8mtqjJgxdFYw8AwGvwPr7PAIAtcYul0NE1JEwALUTA5D88ksqsO/3AmSeKkDm7wX4/aJl3xyVUoFBXXSI7RGI4T2DEB3pDw+Nyv4VrSwD/jxoekp08RhQVgCUXar5WdD8vEWN0frWD0bewaanTb7hNVtn06zZHLVGRASAAajdGIAcT56+3BSGThXgu9/zkX3pisXnGpUSgyP8ENszELE9AzGkqx+0ahkCUV1CAJUltWGo7JLpCZL0vqB+YLpyCRDGln+H0g3wCasTisIBXZfagOQbbmqWU8r8b0FEZAcMQO3EAOT4si+VIfP3AuyreUKUU1Ru8blWrcT13fwR2yMQsT2DMKiLDm4qJ3hSYjSYlge5OiSV5gMlFwD9n4D+vGkryW1ZWFKoAJ9QyydHV7/2CQNUdu5jRURkZQxA7cQA5FyEEPijoExqLss8lY/8EsumJ0+NCtd3C0DvYG90DfBE10BPdA3wRBd/D/mfFLWVoQooyasJRHWCkcXr8/XnOmqQAnD3Bdx1gLuf6aeHX+17D7+a/Q185q7jXElE5BAYgNqJAci5CSFw8kIJMn8vwHcnC7DvdAEKy6oaLKtQAGG+7lIgigz0QkSAJyIDTO/9PN2gcOYh7kYDUHqx4YBU9KfpdXFO2/sqmandGw9P7jrTpvEybW6eTb9mcx0RtREDUDsxAHUsRqPAsVw9Dp8txJn8Upy9VCZtZZVNPx3xcVfXBCPPmmDkJb0P07lD7QzNas0xGmv6H102Nb+VF5p+1ntf2PB7WPl/QtTuNWHI27R4bXOBqe5rN8+aY7wAN4/a1+bzuEq4EgKoLDXdq6py0whED3/OV0UdHgNQOzEAuQYhBPJLKmvCUCnOFlzBmUulyL5UhjMFZbhQXNHk8WqlAp39PUxNagGe6OLviRBfLYJ93BHsq0WwjxY6Dyd/gtQcoxGoLG48HJnflxeZ/iBXlgJVZabRc5Ullq+tHaQaonY3BaO6ocgcnNw86ococxk3D9OxandTc5/a3TS9gVpb85m29nO1O6CywhQNRiNQoTf9G14prPOzyHKf9O991edXT8+gVJtGDXp1Mo0o9AoGvIIafu0ZaJ1rILIzBqB2YgAiALhSaUD25TKcLSjDmUtlNcHI9AQp+/IVVFY33wFZo1Yi2Edbs9UGo9rXpp8BnhrrLQrrjIQAqstNYaiqJihZvG4iOJlfV5mPKaspf6X2tT3CVV1KtWUgcnNv/L1KY7qOq8NMub799TbXo7KklQcqTE+NvIIB75rQJL0OrglRNa+1PrZ7siSEqa9bVZnpv4+qK6at+orpyVb1lTr7yq96XdZ8mepy07UqVaZ/K4XKNK2Eoua9UlW7T/pcVfvT4rUaUCjr7FPXllG61bxX15ZVqk0DD+q+lz5vYXmVpk741tb+92T+b8sFp8hgAGonBiBqjtEokFdcjjMFNc1pBWX4s/AKLhSX44K+AheKK1B0peF+Rw1RKxUI8tZahCIpKPmY9gd4aeCjdYOXVtUxmt7spV64qvlZdaWR4FRWJ1TV7DP/4ayuMP1Bra6o+eNaZ2tvP6rGqD0a6ZCuq31d7/Oa1xovUzipKjf1BTNvJRcaf11WALsHRrINpZtlOJJ+ahrZX+enqmYpImN1zWaofS0Mlu+lzxva18QxQ2cCw+da9ZJb8/ebzziJ2kCpVCBM54EwnQdu6BHYYJnyKgMuFldYhKK6r/P05bhYXIGC0kpUGwVy9eXI1ZcDKGr2+z01Knhr1fB2V8NHq4aPu5v03lurhq+7+bWbqUxNOdPrmrJaNVSu8NRJoahp9vIA0PC9sgqjoSYglTcSmBp5b6gw9XdqLOCorbAmnps74Bdh2lpyHWUFTYekuq+NLQ/6bab2qHlq5lF7L6XmzKtf15R182y+jNrdFJClP86G2tfCUPtHXBhr/3hLnxvrfF7nj7u5rDA0HASM1aZ1BxsMClWtKF9lCt3VlTX/zdX8t1V3agxjFVBZZWqmdkRlBbJ+PQMQkY24u6kQEWDqPN2UKoMR+SUVFiEpT1+Bi1cFp8KyKlTUNLuVVRpQVmlotp9Sczw1KvjUhCYvrRoebirTT40Knle99tSq4alR1WyNv3Z3U3bsfk+NUapMfYY0Td9vh6dUmfoCeQc3X9bcRGVLKjd23m4NQ3WdQFRuCthSMG/oZxOfKRT1m+HqNvlZNNM1tE9dpznwqvMoVab5x2TkEAFo2bJleOmll5Cbm4uoqCi8+eabGDZsWKPlN2zYgPnz5+OPP/5A79698eKLL+K2226TPhdCIDU1Fe+88w4KCwtx4403Yvny5ejdu7c9LoeoVdxUSulpUnMqqg0orTCgpLwa+vIqlFRUo6S8GiUV1Sgur0KxxXvTVlJRJb0vKa9GcUW11H/JHKTy0L4gVZdCAXi6qeChUcNLq4KHmykcadRKaNQqaFQKaNRKuKmU0KiUcFObfmpqfrrVvHZTKaBV177X1H2tUlqcQ6NWQKVUQq1UwE2lhFqlgFqpgFpl2qdWKqBSKlwzmNmSQsF16xyNSg2ovAGtt9w1cXiyB6CPP/4YKSkpWLFiBWJiYrBkyRLEx8fj+PHjCA6u//9AvvvuO0ydOhVpaWn4y1/+gg8//BATJ07E4cOHce211wIAFi9ejDfeeANr1qxB9+7dMX/+fMTHx+OXX36BuzsnbCPnpVWroFWrEODVvj86FdWGekGprLIaZZUGXKk0oPSq16afBlyp2V/3tWmrRnmVKVQJAZTWlMlvbd9bG3NTKaCuCUpqVZ2ApFLATWkKTiqlsqZcTVmVZZBS1zmHSml5jrqhS1UTxlTm4xr5zE2lgFJh2qdUKqCqeW3ezJ+pFAoolYBaqYRKidpjan6q6xyvVFoeo4Ap+NXNf+aX5lBYNxqayzEwUkcmeyfomJgYDB06FEuXLgUAGI1GREREYO7cuXjqqafqlZ88eTJKS0vx5ZdfSvtuuOEGDB48GCtWrIAQAuHh4Xj00Ufx2GOPAQCKiooQEhKC1atXY8qUKc3WiZ2giVrPYBS4UmUKQ2UVpmB0paoapRUGXKkyoLLaiMpqI6oMRlQaTK8rDUZUVQtUGgyoMghpn1Su5meFxXvLcpUG02cGg0CV0Yhqg0C1kZ14bUUKR9J7BZSK2p9KhSmUKaTXkJ6+Nfa55fs6r5W1+8znqRv8TIGv9vzm8KhU1pRX1A2FlqHR9BrSeWrrUVNeYerrpzCfp069VMq611xbtu71KJWm2GnrDKmAwuKe1H6fosF7Vfu67r2sLVjvvqL2WhUW96r2fHXvm2UZ07FA/fusUAA+7m7QeVh3CR6n6QRdWVmJQ4cOYd68edI+pVKJuLg4ZGZmNnhMZmYmUlJSLPbFx8dj06ZNAIDTp08jNzcXcXFx0uc6nQ4xMTHIzMxsMABVVFSgoqK2CUCv17fnsohckkqpkDpXw0feugghYDCaglCVwQiDUaDKIFBdJyBVG0xhymCsG5xqf0qfGUz7zOerW8Z0ntrzmb/HYDSiyiikUCYda1Gm5txG02tjTZ3rvjYKoNpohNFoCpgGIWCs+WkwWr42l7f9v23Nzzo7DJZ7iFrkoVE98URCP9m+X9YAlJ+fD4PBgJCQEIv9ISEh+PXXXxs8Jjc3t8Hyubm50ufmfY2VuVpaWhoWLVrUpmsgIsejUJibuEyd0V2FEKYQZBGoahKLxbN+KcTU/0zUOVfd93XLiTp7hTBtRmH6ztrXpnqY69TQ56b3Na9rAlzd8gbz50ZYhD9zefP1mQJvbZnaAGnab6x7bM331C0r1c9Yt66o/3m9srV1NhivKlfzvbYk6tyguveyoXvYUHCtLdfAOYT5OGF5f1F7v+v+NN83gdp/k7r3uu57AdNPuafzkL0PkCOYN2+exVMlvV6PiIgWDBclInIgpuYbuMb0BkTtJGv8CgoKgkqlQl5ensX+vLw8hIaGNnhMaGhok+XNP1tzTq1WC19fX4uNiIiIOi5ZA5BGo0F0dDQyMjKkfUajERkZGYiNjW3wmNjYWIvyALBjxw6pfPfu3REaGmpRRq/XY//+/Y2ek4iIiFyL7E1gKSkpSEpKwvXXX49hw4ZhyZIlKC0tRXJyMgBg+vTp6Ny5M9LS0gAADz/8MEaOHIlXXnkF48aNw0cffYSDBw/i7bffBmB6BPzII4/gueeeQ+/evaVh8OHh4Zg4caJcl0lEREQORPYANHnyZFy8eBELFixAbm4uBg8ejPT0dKkT89mzZ6Gss6Db8OHD8eGHH+Jf//oX/vnPf6J3797YtGmTNAcQADzxxBMoLS3FrFmzUFhYiBEjRiA9PZ1zABEREREAB5gHyBFxHiAiIiLn05q/31xSmoiIiFwOAxARERG5HAYgIiIicjkMQERERORyGICIiIjI5TAAERERkcthACIiIiKXwwBERERELocBiIiIiFyO7EthOCLz5Nh6vV7mmhAREVFLmf9ut2SRCwagBhQXFwMAIiIiZK4JERERtVZxcTF0Ol2TZbgWWAOMRiPOnz8PHx8fKBQKq55br9cjIiIC2dnZHX6dMV5rx+VK18tr7bhc6Xpd5VqFECguLkZ4eLjFQuoN4ROgBiiVSnTp0sWm3+Hr69uh/yOsi9facbnS9fJaOy5Xul5XuNbmnvyYsRM0ERERuRwGICIiInI5DEB2ptVqkZqaCq1WK3dVbI7X2nG50vXyWjsuV7peV7rWlmInaCIiInI5fAJERERELocBiIiIiFwOAxARERG5HAYgIiIicjkMQDawbNkydOvWDe7u7oiJicGBAweaLL9hwwb069cP7u7uGDhwILZs2WKnmrZdWloahg4dCh8fHwQHB2PixIk4fvx4k8esXr0aCoXCYnN3d7dTjdtu4cKF9erdr1+/Jo9xxntq1q1bt3rXq1AoMHv27AbLO9N93bt3L8aPH4/w8HAoFAps2rTJ4nMhBBYsWICwsDB4eHggLi4OJ06caPa8rf2dt4emrrWqqgpPPvkkBg4cCC8vL4SHh2P69Ok4f/58k+dsy++CvTR3b2fMmFGv7gkJCc2e19nuLYAGf38VCgVeeumlRs/pyPfWVhiArOzjjz9GSkoKUlNTcfjwYURFRSE+Ph4XLlxosPx3332HqVOn4r777sORI0cwceJETJw4ET///LOda946e/bswezZs7Fv3z7s2LEDVVVVGDt2LEpLS5s8ztfXFzk5OdJ25swZO9W4fQYMGGBR72+++abRss56T82+//57i2vdsWMHAOCee+5p9Bhnua+lpaWIiorCsmXLGvx88eLFeOONN7BixQrs378fXl5eiI+PR3l5eaPnbO3vvL00da1lZWU4fPgw5s+fj8OHD+Ozzz7D8ePHcfvttzd73tb8LthTc/cWABISEizqvm7duibP6Yz3FoDFNebk5GDVqlVQKBS46667mjyvo95bmxFkVcOGDROzZ8+W3hsMBhEeHi7S0tIaLD9p0iQxbtw4i30xMTHigQcesGk9re3ChQsCgNizZ0+jZd577z2h0+nsVykrSU1NFVFRUS0u31HuqdnDDz8sevbsKYxGY4OfO+t9BSA2btwovTcajSI0NFS89NJL0r7CwkKh1WrFunXrGj1Pa3/n5XD1tTbkwIEDAoA4c+ZMo2Va+7sgl4auNykpSUyYMKFV5+ko93bChAnilltuabKMs9xba+ITICuqrKzEoUOHEBcXJ+1TKpWIi4tDZmZmg8dkZmZalAeA+Pj4Rss7qqKiIgBAQEBAk+VKSkoQGRmJiIgITJgwAUePHrVH9drtxIkTCA8PR48ePTBt2jScPXu20bId5Z4Cpv+m33//ffzf//1fkwsDO+t9rev06dPIzc21uHc6nQ4xMTGN3ru2/M47qqKiIigUCvj5+TVZrjW/C45m9+7dCA4ORt++ffHggw+ioKCg0bId5d7m5eVh8+bNuO+++5ot68z3ti0YgKwoPz8fBoMBISEhFvtDQkKQm5vb4DG5ubmtKu+IjEYjHnnkEdx444249tprGy3Xt29frFq1Cp9//jnef/99GI1GDB8+HOfOnbNjbVsvJiYGq1evRnp6OpYvX47Tp0/jpptuQnFxcYPlO8I9Ndu0aRMKCwsxY8aMRss46329mvn+tObeteV33hGVl5fjySefxNSpU5tcKLO1vwuOJCEhAWvXrkVGRgZefPFF7NmzB4mJiTAYDA2W7yj3ds2aNfDx8cGdd97ZZDlnvrdtxdXgqd1mz56Nn3/+udn24tjYWMTGxkrvhw8fjmuuuQZvvfUWnn32WVtXs80SExOl14MGDUJMTAwiIyOxfv36Fv2/Kme2cuVKJCYmIjw8vNEyznpfyaSqqgqTJk2CEALLly9vsqwz/y5MmTJFej1w4EAMGjQIPXv2xO7duzFmzBgZa2Zbq1atwrRp05odmODM97at+ATIioKCgqBSqZCXl2exPy8vD6GhoQ0eExoa2qryjmbOnDn48ssvsWvXLnTp0qVVx7q5uWHIkCE4efKkjWpnG35+fujTp0+j9Xb2e2p25swZ7Ny5EzNnzmzVcc56X833pzX3ri2/847EHH7OnDmDHTt2NPn0pyHN/S44sh49eiAoKKjRujv7vQWAr7/+GsePH2/17zDg3Pe2pRiArEij0SA6OhoZGRnSPqPRiIyMDIv/h1xXbGysRXkA2LFjR6PlHYUQAnPmzMHGjRvx1VdfoXv37q0+h8FgwE8//YSwsDAb1NB2SkpKcOrUqUbr7az39GrvvfcegoODMW7cuFYd56z3tXv37ggNDbW4d3q9Hvv372/03rXld95RmMPPiRMnsHPnTgQGBrb6HM39Ljiyc+fOoaCgoNG6O/O9NVu5ciWio6MRFRXV6mOd+d62mNy9sDuajz76SGi1WrF69Wrxyy+/iFmzZgk/Pz+Rm5srhBDi3nvvFU899ZRU/ttvvxVqtVq8/PLL4tixYyI1NVW4ubmJn376Sa5LaJEHH3xQ6HQ6sXv3bpGTkyNtZWVlUpmrr3XRokVi27Zt4tSpU+LQoUNiypQpwt3dXRw9elSOS2ixRx99VOzevVucPn1afPvttyIuLk4EBQWJCxcuCCE6zj2ty2AwiK5du4onn3yy3mfOfF+Li4vFkSNHxJEjRwQA8eqrr4ojR45II59eeOEF4efnJz7//HPx448/igkTJoju3buLK1euSOe45ZZbxJtvvim9b+53Xi5NXWtlZaW4/fbbRZcuXURWVpbF73BFRYV0jquvtbnfBTk1db3FxcXiscceE5mZmeL06dNi586d4rrrrhO9e/cW5eXl0jk6wr01KyoqEp6enmL58uUNnsOZ7q2tMADZwJtvvim6du0qNBqNGDZsmNi3b5/02ciRI0VSUpJF+fXr14s+ffoIjUYjBgwYIDZv3mznGrcegAa39957Typz9bU+8sgj0r9LSEiIuO2228Thw4ftX/lWmjx5sggLCxMajUZ07txZTJ48WZw8eVL6vKPc07q2bdsmAIjjx4/X+8yZ7+uuXbsa/O/WfD1Go1HMnz9fhISECK1WK8aMGVPv3yAyMlKkpqZa7Gvqd14uTV3r6dOnG/0d3rVrl3SOq6+1ud8FOTV1vWVlZWLs2LGiU6dOws3NTURGRor777+/XpDpCPfW7K233hIeHh6isLCwwXM40721FYUQQtj0ERMRERGRg2EfICIiInI5DEBERETkchiAiIiIyOUwABEREZHLYQAiIiIil8MARERERC6HAYiIiIhcDgMQEVELKBQKbNq0Se5qEJGVMAARkcObMWMGFApFvS0hIUHuqhGRk1LLXQEiopZISEjAe++9Z7FPq9XKVBsicnZ8AkRETkGr1SI0NNRi8/f3B2Bqnlq+fDkSExPh4eGBHj164JNPPrE4/qeffsItt9wCDw8PBAYGYtasWSgpKbEos2rVKgwYMABarRZhYWGYM2eOxef5+fm444474Onpid69e+OLL76w7UUTkc0wABFRhzB//nzcdddd+OGHHzBt2jRMmTIFx44dAwCUlpYiPj4e/v7++P7777Fhwwbs3LnTIuAsX74cs2fPxqxZs/DTTz/hiy++QK9evSy+Y9GiRZg0aRJ+/PFH3HbbbZg2bRouXbpk1+skIiuRezVWIqLmJCUlCZVKJby8vCy2559/XgghBADx97//3eKYmJgY8eCDDwohhHj77beFv7+/KCkpkT7fvHmzUCqV0org4eHh4umnn260DgDEv/71L+l9SUmJACC2bt1qteskIvthHyAicgqjR4/G8uXLLfYFBARIr2NjYy0+i42NRVZWFgDg2LFjiIqKgpeXl/T5jTfeCKPRiOPHj0OhUOD8+fMYM2ZMk3UYNGiQ9NrLywu+vr64cOFCWy+JiGTEAERETsHLy6tek5S1eHh4tKicm5ubxXuFQgGj0WiLKhGRjbEPEBF1CPv27av3/pprrgEAXHPNNfjhhx9QWloqff7tt99CqVSib9++8PHxQbdu3ZCRkWHXOhORfPgEiIicQkVFBXJzcy32qdVqBAUFAQA2bNiA66+/HiNGjMAHH3yAAwcOYOXKlQCAadOmITU1FUlJSVi4cCEuXryIuXPn4t5770VISAgAYOHChfj73/+O4OBgJCYmori4GN9++y3mzp1r3wslIrtgACIip5Ceno6wsDCLfX379sWvv/4KwDRC66OPPsJDDz2EsLAwrFu3Dv379wcAeHp6Ytu2bXj44YcxdOhQeHp64q677sKrr74qnSspKQnl5eV47bXX8NhjjyEoKAh33323/S6QiOxKIYQQcleCiKg9FAoFNm7ciIkTJ8pdFSJyEuwDRERERC6HAYiIiIhcDvsAEZHTY0s+EbUWnwARERGRy2EAIiIiIpfDAEREREQuhwGIiIiIXA4DEBEREbkcBiAiIiJyOQxARERE5HIYgIiIiMjlMAARERGRy/n/dr9I7fKkZdkAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:12:29.645111Z","iopub.execute_input":"2024-06-06T18:12:29.645482Z","iopub.status.idle":"2024-06-06T18:12:29.923949Z","shell.execute_reply.started":"2024-06-06T18:12:29.645449Z","shell.execute_reply":"2024-06-06T18:12:29.923088Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T18:12:29.925856Z","iopub.execute_input":"2024-06-06T18:12:29.926458Z","iopub.status.idle":"2024-06-06T18:12:41.868513Z","shell.execute_reply.started":"2024-06-06T18:12:29.926423Z","shell.execute_reply":"2024-06-06T18:12:41.867657Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 326ms/step - accuracy: 1.0000 - loss: 0.0230\nTest Loss: 0.015572687610983849\nTest Accuracy: 1.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### CNN","metadata":{}},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import GlobalAveragePooling2D, InputLayer\nfrom keras.regularizers import l1_l2\n\nmodel = Sequential()\n\nmodel.add(InputLayer(shape=(224, 224, 3))) # input layer\n\n# convolutional block\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\n# convolutional block\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\n# convolutional block\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\n# convolutional block\nmodel.add(Conv2D(128, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(GlobalAveragePooling2D()) # pooling down\n\nmodel.add(Dense(1024, activation='relu')) # dense layer\n\nmodel.add(Dense(1, activation='sigmoid')) # output layer\n\nmodel.summary() # model summary\n\n# model checkpoint callback\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:39:50.507535Z","iopub.execute_input":"2024-06-06T19:39:50.508304Z","iopub.status.idle":"2024-06-06T19:39:50.748828Z","shell.execute_reply.started":"2024-06-06T19:39:50.508271Z","shell.execute_reply":"2024-06-06T19:39:50.747935Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential\"\u001b[0m\n","text/html":"
Model: \"sequential\"\n
\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┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m66,560\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ conv2d (Conv2D)                 │ (None, 224, 224, 32)   │           896 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization             │ (None, 224, 224, 32)   │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d (MaxPooling2D)    │ (None, 112, 112, 32)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_1 (Conv2D)               │ (None, 112, 112, 32)   │         9,248 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_1           │ (None, 112, 112, 32)   │           128 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_1 (MaxPooling2D)  │ (None, 56, 56, 32)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_2 (Conv2D)               │ (None, 56, 56, 64)     │        18,496 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_2           │ (None, 56, 56, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_2 (MaxPooling2D)  │ (None, 28, 28, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_3 (Conv2D)               │ (None, 28, 28, 64)     │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_3           │ (None, 28, 28, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_3 (MaxPooling2D)  │ (None, 14, 14, 64)     │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d        │ (None, 64)             │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (Dense)                   │ (None, 1024)           │        66,560 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (Dense)                 │ (None, 1)              │         1,025 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m133,921\u001b[0m (523.13 KB)\n","text/html":"
 Total params: 133,921 (523.13 KB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m133,537\u001b[0m (521.63 KB)\n","text/html":"
 Trainable params: 133,537 (521.63 KB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m384\u001b[0m (1.50 KB)\n","text/html":"
 Non-trainable params: 384 (1.50 KB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # compiling and fitting model\nhistory = model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=30, batch_size=100,callbacks=[model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:39:50.750383Z","iopub.execute_input":"2024-06-06T19:39:50.750672Z","iopub.status.idle":"2024-06-06T19:47:35.236260Z","shell.execute_reply.started":"2024-06-06T19:39:50.750645Z","shell.execute_reply":"2024-06-06T19:47:35.235122Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Epoch 1/30\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1717702805.424781 543 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n2024-06-06 19:40:22.028703: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[250,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:22.684521: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.655919082s\nTrying algorithm eng0{} for conv (f16[250,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:24.265499: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[250,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:24.898147: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.632754906s\nTrying algorithm eng0{} for conv (f16[250,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:30.114192: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 0: 31136, expected 27232\n2024-06-06 19:40:30.114244: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 1: 31312, expected 27424\n2024-06-06 19:40:30.114254: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 2: 31344, expected 27456\n2024-06-06 19:40:30.114263: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 3: 31344, expected 27440\n2024-06-06 19:40:30.114271: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 4: 31312, expected 27424\n2024-06-06 19:40:30.114279: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 5: 31504, expected 27568\n2024-06-06 19:40:30.114287: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 6: 31440, expected 27520\n2024-06-06 19:40:30.114295: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 7: 31408, expected 27456\n2024-06-06 19:40:30.114303: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 8: 31232, expected 27312\n2024-06-06 19:40:30.114311: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 9: 31216, expected 27328\n2024-06-06 19:40:30.114327: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:705] Results mismatch between different convolution algorithms. This is likely a bug/unexpected loss of precision in cudnn.\n(f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} for eng28{k2=5,k12=39,k13=1,k14=0,k15=0,k17=40,k18=1,k23=0} vs eng27{k2=5,k13=1,k14=0,k18=1,k23=0}\n2024-06-06 19:40:30.114335: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:270] Device: Tesla T4\n2024-06-06 19:40:30.114343: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:271] Platform: Compute Capability 7.5\n2024-06-06 19:40:30.114349: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:272] Driver: 12020 (535.129.3)\n2024-06-06 19:40:30.114356: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:273] Runtime: \n2024-06-06 19:40:30.114379: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:280] cudnn version: 8.9.0\n2024-06-06 19:40:31.206944: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:39.966144: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 9.759366786s\nTrying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:41.937998: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 0: 31136, expected 27232\n2024-06-06 19:40:41.938050: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 1: 31312, expected 27424\n2024-06-06 19:40:41.938060: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 2: 31344, expected 27456\n2024-06-06 19:40:41.938068: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 3: 31344, expected 27440\n2024-06-06 19:40:41.938093: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 4: 31312, expected 27424\n2024-06-06 19:40:41.938102: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 5: 31504, expected 27568\n2024-06-06 19:40:41.938110: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 6: 31440, expected 27520\n2024-06-06 19:40:41.938119: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 7: 31408, expected 27456\n2024-06-06 19:40:41.938127: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 8: 31232, expected 27312\n2024-06-06 19:40:41.938136: E external/local_xla/xla/service/gpu/buffer_comparator.cc:1137] Difference at 9: 31216, expected 27328\n2024-06-06 19:40:41.938155: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:705] Results mismatch between different convolution algorithms. This is likely a bug/unexpected loss of precision in cudnn.\n(f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} for eng28{k2=5,k12=39,k13=1,k14=0,k15=0,k17=40,k18=1,k23=0} vs eng27{k2=5,k13=1,k14=0,k18=1,k23=0}\n2024-06-06 19:40:41.938164: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:270] Device: Tesla T4\n2024-06-06 19:40:41.938172: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:271] Platform: Compute Capability 7.5\n2024-06-06 19:40:41.938179: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:272] Driver: 12020 (535.129.3)\n2024-06-06 19:40:41.938187: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:273] Runtime: \n2024-06-06 19:40:41.938200: E external/local_xla/xla/service/gpu/conv_algorithm_picker.cc:280] cudnn version: 8.9.0\n2024-06-06 19:40:43.023491: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:51.770700: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 9.747312963s\nTrying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,224,224,4]{3,2,1,0}, f16[250,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:54.747770: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:58.405349: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 4.657689528s\nTrying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:40:59.405567: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:41:32.007738: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 33.602274938s\nTrying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:41:34.807953: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:41:36.896114: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 3.088270466s\nTrying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:41:37.896379: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:10.312413: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 33.416197693s\nTrying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,112,112,32]{3,2,1,0}, f16[250,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:11.971461: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[64,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,32]{3,2,1,0}, f16[250,56,56,64]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:12.043028: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.071673903s\nTrying algorithm eng19{} for conv (f16[64,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,32]{3,2,1,0}, f16[250,56,56,64]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:13.777446: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[64,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,32]{3,2,1,0}, f16[250,56,56,64]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:13.811637: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.034383813s\nTrying algorithm eng19{} for conv (f16[64,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[250,56,56,32]{3,2,1,0}, f16[250,56,56,64]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m36/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - accuracy: 0.6986 - loss: 0.5592","output_type":"stream"},{"name":"stderr","text":"2024-06-06 19:42:36.003929: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[165,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:36.060798: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.056963792s\nTrying algorithm eng0{} for conv (f16[165,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:37.425712: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[165,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:37.485178: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.059572805s\nTrying algorithm eng0{} for conv (f16[165,56,56,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,56,56,64]{3,2,1,0}, f16[64,3,3,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:41.832006: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,224,224,4]{3,2,1,0}, f16[165,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:46.892058: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 6.06015414s\nTrying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,224,224,4]{3,2,1,0}, f16[165,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:49.211378: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,224,224,4]{3,2,1,0}, f16[165,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:54.330687: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 6.119403237s\nTrying algorithm eng0{} for conv (f16[32,3,3,4]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,224,224,4]{3,2,1,0}, f16[165,224,224,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:56.638876: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:57.438459: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.799660504s\nTrying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:42:58.438793: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:43:18.445467: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 21.00689833s\nTrying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:43:20.651114: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:43:21.435956: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.7850167s\nTrying algorithm eng19{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:43:22.436156: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n2024-06-06 19:43:42.431187: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 20.995130514s\nTrying algorithm eng0{} for conv (f16[32,3,3,32]{3,2,1,0}, u8[0]{0}) custom-call(f16[165,112,112,32]{3,2,1,0}, f16[165,112,112,32]{3,2,1,0}), window={size=3x3 pad=1_1x1_1}, dim_labels=b01f_o01i->b01f, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.7007 - loss: 0.5570 \nEpoch 1: val_accuracy improved from -inf to 0.61649, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 3s/step - accuracy: 0.7027 - loss: 0.5550 - val_accuracy: 0.6165 - val_loss: 0.6627\nEpoch 2/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.8647 - loss: 0.3232\nEpoch 2: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 0.8651 - loss: 0.3223 - val_accuracy: 0.3874 - val_loss: 0.8143\nEpoch 3/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9478 - loss: 0.1691\nEpoch 3: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9479 - loss: 0.1687 - val_accuracy: 0.3874 - val_loss: 1.0491\nEpoch 4/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - accuracy: 0.9603 - loss: 0.1315\nEpoch 4: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 200ms/step - accuracy: 0.9605 - loss: 0.1311 - val_accuracy: 0.3874 - val_loss: 1.7835\nEpoch 5/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - accuracy: 0.9797 - loss: 0.0725\nEpoch 5: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 200ms/step - accuracy: 0.9797 - loss: 0.0725 - val_accuracy: 0.3874 - val_loss: 2.8614\nEpoch 6/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - accuracy: 0.9856 - loss: 0.0513\nEpoch 6: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 0.9855 - loss: 0.0516 - val_accuracy: 0.3874 - val_loss: 2.5944\nEpoch 7/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - accuracy: 0.9872 - loss: 0.0440\nEpoch 7: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 200ms/step - accuracy: 0.9872 - loss: 0.0440 - val_accuracy: 0.3874 - val_loss: 4.3719\nEpoch 8/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9950 - loss: 0.0248\nEpoch 8: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 0.9951 - loss: 0.0247 - val_accuracy: 0.4031 - val_loss: 3.2178\nEpoch 9/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9965 - loss: 0.0149\nEpoch 9: val_accuracy did not improve from 0.61649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 198ms/step - accuracy: 0.9965 - loss: 0.0149 - val_accuracy: 0.4542 - val_loss: 2.1462\nEpoch 10/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9938 - loss: 0.0179\nEpoch 10: val_accuracy improved from 0.61649 to 0.66623, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 201ms/step - accuracy: 0.9938 - loss: 0.0180 - val_accuracy: 0.6662 - val_loss: 1.4797\nEpoch 11/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9879 - loss: 0.0361\nEpoch 11: val_accuracy did not improve from 0.66623\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 0.9880 - loss: 0.0361 - val_accuracy: 0.4228 - val_loss: 4.2342\nEpoch 12/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9921 - loss: 0.0263\nEpoch 12: val_accuracy improved from 0.66623 to 0.80366, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 200ms/step - accuracy: 0.9921 - loss: 0.0262 - val_accuracy: 0.8037 - val_loss: 0.6434\nEpoch 13/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9972 - loss: 0.0093\nEpoch 13: val_accuracy improved from 0.80366 to 0.84555, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 201ms/step - accuracy: 0.9972 - loss: 0.0093 - val_accuracy: 0.8455 - val_loss: 0.5716\nEpoch 14/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9991 - loss: 0.0045\nEpoch 14: val_accuracy did not improve from 0.84555\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9991 - loss: 0.0045 - val_accuracy: 0.7997 - val_loss: 0.4589\nEpoch 15/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9989 - loss: 0.0043\nEpoch 15: val_accuracy did not improve from 0.84555\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9989 - loss: 0.0043 - val_accuracy: 0.7932 - val_loss: 0.7100\nEpoch 16/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9992 - loss: 0.0030\nEpoch 16: val_accuracy improved from 0.84555 to 0.86649, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 201ms/step - accuracy: 0.9992 - loss: 0.0030 - val_accuracy: 0.8665 - val_loss: 0.4303\nEpoch 17/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - accuracy: 0.9987 - loss: 0.0046\nEpoch 17: val_accuracy did not improve from 0.86649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 200ms/step - accuracy: 0.9987 - loss: 0.0046 - val_accuracy: 0.8154 - val_loss: 0.7979\nEpoch 18/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9995 - loss: 0.0040\nEpoch 18: val_accuracy did not improve from 0.86649\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9995 - loss: 0.0041 - val_accuracy: 0.8613 - val_loss: 0.3403\nEpoch 19/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9945 - loss: 0.0194\nEpoch 19: val_accuracy improved from 0.86649 to 0.89398, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 201ms/step - accuracy: 0.9945 - loss: 0.0195 - val_accuracy: 0.8940 - val_loss: 0.6603\nEpoch 20/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9968 - loss: 0.0141\nEpoch 20: val_accuracy did not improve from 0.89398\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 0.9969 - loss: 0.0140 - val_accuracy: 0.8809 - val_loss: 0.4618\nEpoch 21/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9984 - loss: 0.0052\nEpoch 21: val_accuracy did not improve from 0.89398\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9984 - loss: 0.0053 - val_accuracy: 0.8704 - val_loss: 0.5346\nEpoch 22/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9992 - loss: 0.0034\nEpoch 22: val_accuracy improved from 0.89398 to 0.98037, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 202ms/step - accuracy: 0.9991 - loss: 0.0034 - val_accuracy: 0.9804 - val_loss: 0.0484\nEpoch 23/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9992 - loss: 0.0029\nEpoch 23: val_accuracy did not improve from 0.98037\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9992 - loss: 0.0029 - val_accuracy: 0.9031 - val_loss: 0.2403\nEpoch 24/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - accuracy: 0.9994 - loss: 0.0021\nEpoch 24: val_accuracy did not improve from 0.98037\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 0.9994 - loss: 0.0021 - val_accuracy: 0.8390 - val_loss: 0.4961\nEpoch 25/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 0.9997 - loss: 0.0017\nEpoch 25: val_accuracy improved from 0.98037 to 0.99869, saving model to model.keras\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 201ms/step - accuracy: 0.9997 - loss: 0.0016 - val_accuracy: 0.9987 - val_loss: 0.0100\nEpoch 26/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - accuracy: 1.0000 - loss: 4.9018e-04\nEpoch 26: val_accuracy did not improve from 0.99869\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 199ms/step - accuracy: 1.0000 - loss: 4.8887e-04 - val_accuracy: 0.9974 - val_loss: 0.0079\nEpoch 27/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - accuracy: 1.0000 - loss: 3.9999e-04\nEpoch 27: val_accuracy did not improve from 0.99869\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 198ms/step - accuracy: 1.0000 - loss: 3.9971e-04 - val_accuracy: 0.9974 - val_loss: 0.0066\nEpoch 28/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - accuracy: 1.0000 - loss: 1.8080e-04\nEpoch 28: val_accuracy did not improve from 0.99869\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 196ms/step - accuracy: 1.0000 - loss: 1.8042e-04 - val_accuracy: 0.9974 - val_loss: 0.0070\nEpoch 29/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - accuracy: 1.0000 - loss: 1.5811e-04\nEpoch 29: val_accuracy did not improve from 0.99869\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 196ms/step - accuracy: 1.0000 - loss: 1.5819e-04 - val_accuracy: 0.9974 - val_loss: 0.0081\nEpoch 30/30\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - accuracy: 1.0000 - loss: 1.2925e-04\nEpoch 30: val_accuracy did not improve from 0.99869\n\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 197ms/step - accuracy: 1.0000 - loss: 1.2944e-04 - val_accuracy: 0.9974 - val_loss: 0.0062\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:47:53.239675Z","iopub.execute_input":"2024-06-06T19:47:53.240070Z","iopub.status.idle":"2024-06-06T19:47:53.435350Z","shell.execute_reply.started":"2024-06-06T19:47:53.240041Z","shell.execute_reply":"2024-06-06T19:47:53.434423Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:47:53.658642Z","iopub.execute_input":"2024-06-06T19:47:53.658956Z","iopub.status.idle":"2024-06-06T19:47:53.932078Z","shell.execute_reply.started":"2024-06-06T19:47:53.658929Z","shell.execute_reply":"2024-06-06T19:47:53.931113Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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SMlIiKi1kbWBTpHjhyJkSNHNvn4FStWIDo6Gm+++SYAoGvXrti5cyfeeustJCYmWipMIiJyEDqdQJVOQKsTqNLpoNMBAgJCAAKAEKL6XtoPw/66xzkyFyclgrxc5Q6jda1CnpqaioSEBKN9iYmJeOKJJxp8TXl5OcrLyw2PNRqNpcIjcjhVWh1KKrUorZBuJRValFZWoUS/XX1fUlGFskqtYb9W1/wfAOMfkuo9or4fGeMfIUD64VEoFFApFXBS1r5XwkllvF+lUEClqvW8UgGlUmE4j04noBOATkjX1Inaj42f0z92V6vQOcgLMSFeaOvnZtJqynITQqBSK1Cp1VXfam/rUFFV87hC/3xV9fM6Aa1OhyqtPnmofa+T7rUN7Nc/1ta///qkxOj82vr3a3XXx1HzfO3cpLviPLopL6JYuKIEapQIVxTDFSVwRbFwRSnUKIEaWqjk+w9jo/q288XGx26WO4zWleRkZGQgODjYaF9wcDA0Gg1KS0vh5uZW5zXJycl44YUXrBUitVJaXd1/vCuqrnus1VX/o11zrEqpQKiPG8J8XeHj5tyqfrQaU1apxeVrJUjLK8GlvNLq+xJculaK9IJSlJRrUaHVyR1mq+WldkLnECnh6RrihZgQb8SEeMHHzbnZ5xRCIKuwHGl5JbiYW2L4b3YxtxjZReVSq0StBExnSMCk1gmdrr6ETbqvakFi2hq5owzrXV6Eu6K80WPLhLMh+SmtTob02zoHHd9TXBwNgEmOxS1YsABJSUmGxxqNBhERETJGRHIqr9Li6OUC7Dmfh73n83Aw7RqKyqtgjn+/3V1UCPN1Q6iPK8J93QzJT5ivm2G/q7Pt/MWn1QlkaMpwKU/6MbxcncDofxizChv/x11PqQDcXZzg5qKCu4sKbs6qWttOdfY5qVr2D78CgEIBKKCovq9+XJ1k1vtc9WMhAK2o/Zf+dS0Gtf+yr9PyoIMCCiiV0rWUCgWUCkCpkM5t/LhmW1kdW35JBU5mFOJsdhEKy6uw/+I17L94zei9hfm4IqY66ekS4oUuoV5oH+AJFyfpM6udfKblluCiIZEpwaVrJSirtF7i6eKkhItKCWeVAs4qJZxVSrg41Tx2UinholLAqbqlrHarmKGVTN+yZmhJUxq3qClrXl9vC5zy+tcb76/vXHWPv+66KgVcru6D62flEM7uEGF9oKgoBvS3ymIoyosAoQUAuCoq4YpKtEGh9MHYz986zednGx9Cq0pyQkJCkJmZabQvMzMT3t7e9bbiAIBarYZarbZGeGSDSiu0OJh2DbvP52Hv+VwcTMtHeVXjPwIqpcLwD7VL9T/ezk7XPVYpUF6lQ0ZBGXKLK1BSocWZrCKcySpq8LxtPFwMCU+Yrxva+rkhwt8d7fzdEeHvDk+1+f+XzCuuwNnsIpzLLsLZ7GKcyy7CuexiXLpWgkrtjbM7L7UTIvzdEeHvZogxwt8d4b5u8FRXJy8uKriolHbVimVplVodzmUX42SGBiczCnGq+nYlvxRXC8pwtaAM209lG453UioQFeCBorIqZGjKbnhulVKBMF9XRPp7GL5bkW3cEeythpNSaZSQKa5LwmqSsurnlNXPQfr/wUlVk9SolAr7/m+edwIAoIi8CYoHv6r7vBCAtqI68SkCKkpqbdckQ3DU2hzPILkjANDKkpz4+Hh8//33Rvu2bt2K+Ph4mSIiW6Mpq8T+C9eqW2pyceRyQZ1m9jYeLhgY7Y+B0f4YEOWPIC919V+dNX+NqpSm/eNdVqlFekEZruaXVt+qtwukx+kFZSip0CK3uAK5xRU4eqWg3vP4e7ggojrxMSQ/ftJ9qK8rnBtoAanU6nAxt8Q4kckpxtnsIuSXVDYYt5NSYUi0ImpdS5/U2FsXnK1wVimrW2u8MLbW/oLSSvyVWYiTGYU4ma4xJD+F5VVGybOn2gntaiUwEdX37fzdEebr1uD3hEyQ+ad0H9St/ucVCsBJLd3c/a0XF5lE1iSnqKgIZ86cMTw+f/48Dh06BH9/f7Rr1w4LFizAlStX8NlnnwEAHn30Ubz33nv417/+hRkzZmDbtm348ssvsXnzZrneAsksS1OGA2nXDN1Pf6Zr6vzhFOLtirj2/oiLboOB0f7oEOhh9h9uV2cVogM8EB3gUe/zQggUlFYakp/0glJczi/F5WuluFzdXXStpBJ5xRXIK67A4ct1kyClAgjzdTMkIl6uTrhQndik5ZXcsGYi3NcN7QM90CHQEx0CPdA+0BORbdwR6uNmckJHluPj5owBUVLyrSeEwJX8UpzNLoa3qxMi23jAz53Jp8VlHpfug7vLGwe1iKxJzr59+zB06FDDY33tzNSpU7Fq1Sqkp6cjLS3N8Hx0dDQ2b96M+fPn4+2330bbtm3x0Ucfcfi4AxBCIC2vBMevanDsSgGOX9Xg+FUNcorq1o1EtXGvbqlpg7hof5sYxaJQKODr7gJfdxd0C/Ou95jCskpDgW9NwW91vcy1UpRX6aSk6FopUs/l1nm9u4sK7QM90D7AEx0CPQ1JTXSAB9xcbKcWiEyjUCjQ1s8dbf3c5Q7FcQgBZDHJsQcK4WCD+TUaDXx8fFBQUABv7/p/bEheVVodzmQX4fgVTXUyU4A/r2pQWF5V51ilAugU5GXofhoY7Y9gb/nnZjA3nU4gu6jckPRcyitFQWklogLcpaQmyAMh3q6yJ3NEdqHgCvBWN0ChAp5Nl7qkSHbN+f1uVTU5ZF8qqnTILS5HekEZTqRrcOyKBn9eLcDJjMJ6i4NdqusYuod5o3u4D7qHeaNriLdDtFIolQoEe7si2NsV/aPY/09kUfquqoDOTHBaOSY5ZFZanUBucTmyC8uRU1SB7EL9drnxdlH5DQtiPdVO6BbqjW5h3uge5o0e4T7oGOTJgkoisjxDV1UDRcfUajDJoRa5kFOM5TvO4vDlfOQUlSO3uMKkEZNOSgUCvdToFFzdQhPmje5hPoj0dzfMLktErViZBsg6AbSLkzuSpmPRsd1gkkPNcimvBO9uO42vDlypM0W/QgG08VAjwNMFgV7qmptnzX1A9b2PmzOTGSJ79v0/gSNrgQkfAz3vkTuapjEMH2eS09oxySGTXMkvxXvbzmD9vkuGIctDYwIxOS4SYb5uCPBygb+7S4tntCUiO6DTAn/9IG3vX9U6kpyqCiDnlLTNlpxWj0kONUl6QSne334Wa/9IM8ySO6RTAOYP74y+7fxkjo6IbFLGEaCses6nC78B+WmAbzt5Y2pM7mlAVwWofQCftnJHQy3EJIduKEtThvd3nMUXe9NQUT3i6aYObTB/eGejCcuIiOo4/6vx4yPrgFv+KU8sTZVZq+iYUzK0ekxyqF7ZheX4zy9n8fnui4bh3AOj/DF/eGfEd2gjc3RE1Crok5zQ3kD6IeDwWmDIk7adPOiTnIaWc6BWhUkOGckrrsB/fj2Lz36/iNJKaYXdvu188Y87YnBThzacbI6ImqaqAriYKm2PfB34fByQewa4sh9o21/W0G4oq7romMPH7QKTHAIA5JdU4MPfzmHVrgsorpCSm9gIXyQN74xbOgUwuSEi01w9IK3C7d4GaDsA6HqXNMrq8BrbTnIM3VU95I2DzIJJjoMrLKvER7+dx8c7z6OoetmEHuHeSBreGUNjgpjcEFHz6LuqooYASiUQO0lKco59BSS+YpszCZdeAzRXpO2grvLGQmbBJMdBlVZo8WnqBaz45axh5uEuIV5IGt4Zw7sFM7khopbRJznRt9Tce4UBhVeB0z8BXcfIF1tD9PPj+LQDXH3kjYXMgkmOgymv0mLt3kt4b/sZZBdKK3h3CPRA0vAYjOwRwon5iKjlKkuBS3uk7ehbpXulCuh1H7BrqVSAbItJDutx7A6THAdRpdVh44EreDvlNK7klwIA2vq54YmEzhjXO4yT9xGR+VzaA2grpJabNh1q9sdOkpKcv34EinMBDxsbqZl5TLrnJIB2g0mOndPpBDYfTcdbW//CuZxiAECQlxqPD+uEif0j4OLE5IaIzKx2V1Xtru+grjXDyY9vBAbOlCO6hhmWc2BLjr1gkmOnhBBIOZGFN7f+hRPpGgCAn7szHrutIx6Kj4Srs0rmCInIbl1fj1Nb7KTqOXPW2FaSo9PV6q7iyCp7wSTHDu06k4P/+/EUDl3KBwB4qZ0w85b2mDE4Gp5q/icnIgsq0wBXDkjb0UPqPt/jHuDHZ6X5crL/AgI7Wze+hhSkARVFgMrFuIuNWjX+4tmR/Rev4Y0fTyH1XC4AwM1ZhWk3R+Fvt7SHr7uLzNERkUNISwWEFvCLrn+dKs9AoNNw4K8t0pDyYYusH2N99PPjBMQAKmd5YyGzYZJjB7Q6gaQvD+GbQ1cBAC4qJR6Ia4fHhnZAkJerzNERkUO5UVeVXuwkKck5vA4Y+pw0j47c9PU4LDq2K0xy7MD2k1n45tBVqJQK3NuvLR4f1gnhvm5yh0VEjuj8L9L9jZKcziOlVb41l4GLO298rLVk1VqYk+yGDaTP1FJr9qYBAGbcHIVXJ/RigkNE8ijJAzKOSts3SlycXYEe46Xtw2stH1dTGJZzYEuOPWGS08pdzS/F9lNZAID7B9bT/01EZC0XfpPuA7sCnkE3Pjb2fun+z2+AimLLxtWYyjJp8VAACGKSY0+Y5LRyX+67BJ0ABrX3R/tAT7nDISJH1pR6HL2IOMAvShrRdHKzRcNqVPZJQOgAN3/AK0TeWMismOS0YlqdwLo/LgFgKw4R2QBTkhyFoqY15/Aay8XUFFm1io65bp9dYZLTiv3yVxbSC8rg5+6MxO7864OIZKRJB3L+AqAAom5u2mt6TZTuz+0ANFctFVnjWI9jt5jktGJf7JFacSb0bcsZjIlIXvp6nNBYwM2vaa/xjwbaxUtdRUfXWy62xuiTHC7nYHeY5LRSGQVl2HYyEwAwiV1VRCS3pgwdr0/sJOn+0BpACPPG1FSGlhwu52BvmOS0UvqC44HR/ugYxIJjIpKZoR7nVtNe120coFID2SeAjCNmD6tRxTlAcRYABRDUxfrXJ4tiktMK1S44foCtOEQkt2sXgPw0QOkEtBtk2mvdfIEuo6RtOebM0bfi+EUBLh7Wvz5ZFJOcVujX09m4kl8KHzdnjOjBgmMikpm+FSe8P6BuRsuyfpTV0fWAttJ8cTUFi47tGpOcVmjNHmmGYxYcE5FNMGXoeH063A54BALF2cDZbeaLqymymOTYMyY5rUympgwpJ/UzHEfIHA0ROTwhWp7kqJyBnvdK29aeM4ctOXaNSU4rs37fJWh1AgOi/NAp2EvucIjI0eX8BRRlAk6uQNsBzT+PfpTVye+B0nyzhNYonRbIOiltczkHu8QkpxXR6QTW7OUMx0RkQ/StOBFx0sKbzRXSS5qnRlsO/Pm1WUJrVN55oKoUcHKT5uwhu8MkpxX57UwOruSXwtvVCaN6hsodDhFR8+fHuZ5CUdOaY61RVvp6nKAugJL1jfaISU4roi84vpsFx0RkC3Q64Hz1TMemzo9Tn573AQolkJYK5J1r+fkaw3ocu8ckp5XI0pTh5xPSDMfsqiIim5B5FCjLB1y8gLA+LT+fd2hNsnTky5afrzGG5RyY5NgrJjmtxPr9l1GlE+gX6YeYEBYcE5EN0NfjRN4EqJzMc87aK5NbepkHw+rjXLPKXjHJaQV0OoG1f0hdVWzFISKb0dKh4/Xpeifg7CHNonxpj/nOe72KYqnwGOCaVXaMSU4rsOtsDi7llcLL1QmjWXBMRLZAWwlc/F3aNmeS4+IBdBsrbVtyzpyskwAE4BEEeARY7jokKyY5rcCavdUFx33C4ebCgmMisgFXDwIVRYCbn/lbQvSjrI5tAirLzHtuvcxj0j2Lju0akxwbl11Yjp+OVxccx7GriohshH7oeNQQQGnmn5KoIYB3W6C8APjrB/OeW89Qj8Mkx54xybFxG6oLjvu080WXEG+5wyEikliiHkdPqQR63SdtW2rOHA4fdwiyJznLli1DVFQUXF1dERcXh7179zZ4bGVlJV588UV06NABrq6uiI2NxZYtW6wYrXWx4JiIbFJlGZBWXRRsjvlx6qPvsjrzM1CUbd5zC1Fr+DhHVtkzWZOcdevWISkpCYsXL8aBAwcQGxuLxMREZGVl1Xv8c889h//85z9499138eeff+LRRx/F+PHjcfDgQStHbh2p53JxMbcEXmon3NmLBcdEZCMu75WWX/AMAQI6WeYagTFAWF9AVwUc+8q85y7MAErzpIkHA7uY99xkU2RNcpYsWYKZM2di+vTp6NatG1asWAF3d3esXLmy3uM///xzPPPMMxg1ahTat2+P2bNnY9SoUXjzzTetHLl1fFFdcDyuTzjcXcw0BwURUUvV7qpSKCx3Hf2cOYdWm3fOHP1yDm06tmy9LbJ5siU5FRUV2L9/PxISEmqCUSqRkJCA1NTUel9TXl4OV1fjL6Sbmxt27txp0VjlkFNUjp+OZwBgVxUR2RhL1uPU1mMCoFIDGUfMO2cO63EchmxJTk5ODrRaLYKDg432BwcHIyMjo97XJCYmYsmSJTh9+jR0Oh22bt2KjRs3Ij09vcHrlJeXQ6PRGN1ag6/2X0alViA2whfdwlhwTEQ2orwQuLJf2rZ0kuPRpqYAOXWZ+c6bWT2yiss52D3ZC49N8fbbb6NTp07o0qULXFxcMHfuXEyfPh3KGwxfTE5Oho+Pj+EWERFhxYibRwhhmBvngYG2Hy8ROZC03VKdjG8k4Bdp+esNeky6P/mdNAuyOei7q7icg92TLckJCAiASqVCZmam0f7MzEyEhITU+5rAwEB8/fXXKC4uxsWLF3Hy5El4enqiffv2DV5nwYIFKCgoMNwuXbpk1vdhCanncnEhtwSeaifc2StM7nCIiGro58exdCuOXnA3oP1QQOiAPR+0/HzaSiD7VPW52ZJj72RLclxcXNCvXz+kpKQY9ul0OqSkpCA+Pv6Gr3V1dUV4eDiqqqrw1VdfYezYsQ0eq1ar4e3tbXSzdWv2SonY2N5h8FCz4JjI4V36A3g7Flh9H3Bhl+UXrrwRQz2OhYaO1yd+jnR/4DOgrIUlB7lnAW0F4OIJ+LDe0d7J2l2VlJSEDz/8EJ9++ilOnDiB2bNno7i4GNOnTwcATJkyBQsWLDAcv2fPHmzcuBHnzp3Db7/9hhEjRkCn0+Ff//qXXG/B7HKLyrHlmFRjxIJjIkLeOWDNRKmr5vSPwKpRwMfDgRP/A3Ra68ZSkgekH5G2o4dY77odhgEBnYGKQuDg5y07l345h6Bu5p+pmWyOrP+FJ06ciDfeeAOLFi1C7969cejQIWzZssVQjJyWlmZUVFxWVobnnnsO3bp1w/jx4xEeHo6dO3fC19dXpndgfl8dkAqOe7X1QY9wH7nDISI5leQBq+8FSnKB0Fig33RptNHlP4B1DwLLBgL7V1lufafrXdwFQAABMYBX/WUFFqFU1tTm7FnRsuTOsJwD63EcgUIIOds9rU+j0cDHxwcFBQU213UlhMDtb/6C8znFSL67J1tyiBxZVTnw2Tgg7XdpHaeZKVJiUZQF7PkP8MeHQFmBdKxnMBD3KNB/BuDma7mYvv8nsPcDYMBMYPQblrtOfSpLgSXdpEn87vusZqVyU30xEfhrCzDqDWDgTPPGSBbVnN9vttXZkN3n8nA+pxgeLiqMiWXBMZHD0umArx+TEhy1NzB5fU3LiWcQMGwhMP84kPgK4B0OFGUCKS8Ab3UHfnwWKLhimbisNT9OfZzdpCQOAFLfb/55DMPH2ZLjCJjk2BD9sPG7eofDkwXHRI5r+8vAsQ2A0klqtaiva0XtJRXkzjsMjP+P9KNdUQSkvge83QvYNBvIOmG+mAozgeyTABRA1GDzndcUA2cCSmfg0u6auXpMUVYAFEj/zrK7yjEwybERecUV2HJMmgTxAXZTETmuA58Dv1V3BY15G+gw9MbHq5ylxSxn/w48sB6IHCzNY3P4C+D9QVL3zMXfWz4i68Jv0n1IT8Ddv2Xnai6vEGkWZKB5rTn6pM87HHDzM19cZLOY5NiIjQcuo0KrQ49wb/Rsy4JjIod0dhvw3RPS9i3/BPo82PTXKhRA5zuA6ZuBR1KArncBUEj1J5+MBD5KkIqU9XU8prL2/DgNia8uQP7za6Dgsmmv5crjDodJjo3Qt+Lc158zHBM5pMzjwJdTpVaYnvcCQ59t/rna9gcmfg48vh/oN00akXVlH/C/ecAbnYH104HTWwFtVdPPKcf8OPUJja1prdpr4uSAXLPK4TDJsQGFZZU4eCkfADA0JkjeYIjI+jTp0kR/5Rog8mZg7DLzrO7dpoPU5TX/GJDwAhDYBagqA45vBFbfA7zVTSpU1v/4N+TaRWmeHoUKiLzxZK1WoZ8ccP8qoLyo6a8zDB9nkuMomOTYgD3n8qDVCUS1cUeEv7vc4RCRNZUXSZP9aS4DbToBE/8LOKnNew3PIGDwE8Bju4FZO6Th5u5tpFFZqe8By28CVgyW6lyKsuq+Xl+PE95PKniWW+cRgH97qevt8JqmvUaImpFVTHIcBpMcG7DzTA4A4OaOATJHQkRWpa0CNswA0g8D7gHSUHFLFvUqFEBYH2Dka0DSSWDSGqDrGGnEUsZR4McFwJtdpFal45tqJhk8ZyP1OHpKJRA3W9revVwact+YgstAeYE0Yq1NJ8vGRzaD45RtgD7JGdKJSQ6RwxAC2PKUtFSDkytw/1rAP9p613dyAbqMkm4lecCxr6RWkSv7pZhO/wi4+gDd7wbObZdeYytJDgD0fgDY/m8g76wUa8zIGx+v75ILiJHeOzkEtuTILL2gFGeyiqBUAPHtmeQQOYzUZcAfHwFQAHd/AEQMkC8Wd39pDpqZ24A5fwCDk6Rh1mUFwP5PgOJsqXg5YqB8MV5P7Qn0nSptpy5r/PgsfdExR1Y5EiY5Mtt1JhcA0LOtL3zcnWWOhois4s9vgZ+ek7bveKn5SxRYQmBnIGEx8MRRYMo3QK9JUotO3ynSrMO2JO5vUjH0hd9qFg5tCEdWOSR2V8lsV3VX1eCObWSOhMjOCQF8N19a7HLCx/J1WVzeB2ycCUAAAx4B4ufKE0djlCqg/W3SzVb5tJUSxOMbgd3vA+NXNHysYTkHJjmOhC05MhJCsOiYyFquHpS6Xk58C+z7WJ4Y8s5LMxBXlQGdEoERr5lnqLgj0yeJRzcAhRn1H1NVDuSelrbZkuNQmOTI6K/MImQXlsPVWYl+kZxinMiiDn5es/3La0DpNetevyQPWH0vUJIDhPQC7lkJqNiY3mJt+wERcYCusrrGqR45f0mTB7r6AN5c/NiRMMmR0W+nswEAA6PbQO2kkjkaIjtWUSL9pQ9IaxaVXgN+e9N61xcC2DBdak3wbgs88KVUOEvmMah6qYd9K4HK0rrP1+6qYsuZQ2GSIyN9Pc4QdlURWdaJb6XZhH0jgfHVSwHs+Y80i681HPoCOLcDcHIDJn8JeIda57qOosudgE87qd7qyLq6z2cek+7ZVeVwmOTIpKJKhz3n8wCwHofI4g5Ud1X1eRDoNBxoPxTQVgApL1r+2iV5NSOpbnuaP7SWoHKSRloB0uSA16+4bljOgcPHHQ2THJkcTLuGkgotAjxd0CXEBqZJJ7JXuWeBizsBKKQJ5BQKadg2FNIEeJf3Wfb6WxcCpXnSytf6NZfI/Po+BLh4AtkngbMpxs8Zho/3sH5cJCsmOTLRd1Xd1CEASiX7iIks5uB/pfuOw6QhxwAQ0hPoM1na/vHZun/5m8vF32uuf+dbgIpzYVmMfi4fQFqDS68kDyhMl7aDulo/LpIVkxyZ/GaYH4ddVUQWo62S6mEAoM9Dxs8NfRZwdgcu7QZO/M/8166qAL5Lkrb7TgHaDTL/NchY3N8AhVJqyck6Ie3Tt+L4RtrG4qJkVUxyZKApq8ThS/kAgJu5XhWR5Zz5GSjKkFbcjhll/Jx3GHDT49L2z4ulpMScUt8Dsk9I1054wbznpvr5RQFdRkvbu6tbc7K48rgjY5Ijg9SzudAJoH2AB8J9bWyadCJ7op8bp9ek+mc4vunvgEcQkHdOGn5sLtcuAL+8Lm3f8bJlVxYnY4Oq654OrwOKcziyysExyZGBYSkHtuJQa1RWIK29pK2SO5IbK8oC/toibfd9qP5j1J7A7c9K27+8CpTmt/y6QgDf/xOoKgWihgCxk1p+Tmq6doOAsD6AtlxKXA1z5HBklSNikiODnae5lAO1UjodsOZ+4MuHgJTn5Y7mxg6vkWa5De9/44LT3g8CgV2rJwh8o+XXPfEtcPonQOkMjF7CyeesTaGoac3Z+2FNbQ5HVjkkJjlWdiW/FOdyiqFUAIPac1FOamX2rAAu7pK2U98HMo7JG09DhKiZG6ehVhw9lVP1kHK0fILA8kLgh6ek7cFPSCt6k/V1Hwd4hQHFWUBlMaBSA/7t5Y6KZMAkx8p2VbfixEb4wseNw0mpFck5DaRUF9D6tAOEFvjuCal1x9Zc2iMtoeDsDnS/u/HjOyaYZ4LAbS9Lw5X9ooEh/2j+eahlVM5A3Kyax4ExXCfMQTHJsbKdHDpOrZG2Ctj0qLR6dofbgRlbpInXLv8BHPhU7ujq0rfidB8PuHo3frw5Jgi8egjY+x9pe/QbgDMHFciq3zQpyQXYVeXAmORYkU4naoqOmeRQa/L7O8CVfYDaG7jrXcAnHLi9eqmCnxdLRb62orwQOL5J2r5+bpwbCekJ9K6eIPCn50ybIFBX3aoldFLLUceEpr+WLMPNDxjwiLTd/lZ5YyHZMMmxopMZhcgtroC7iwp92vnJHQ5R02T+CexIlrZHvFoza/CAmUBorDTaSr82ky04tlGqw2jTyfQJ+G5/VlpEMy3VtAkC960Erh6UksARyaZdkywn4QVgdirQa6LckZBMmORYkb4VJy7aHy5O/OipFdBWApv+JtWqdB4prf2kp3KSliqAQlr5+dwOuaI0drDWYpymjmxqzgSBhRk1dTzDFgFeIaZdkyxHqZQW5eQIN4fFX1or0i/lwKHj1Gr8+gaQcURq+h/zdt0fi/B+wMCZ0vZ3SUBlmfVjrC3rpFQnpFABsfc37xw3mzhB4JYFQLkGCOsL9J/RvGsSkUUwybGS8iot9p7PBcBJAKmVuHqoZt6YUW8AXsH1H3f7c4BnMJB3Ftj1ttXCq5e+FafziIbjbYzaCxj6jLTd2ASBZ34Gjm+U1ku68y1AqWreNYnIIpjkWMn+i9dQVqlDgKcaMcFcJI5sXFW5NJpKVwV0Gwv0mNDwsa4+NXUov70J5J61TozXq6qQJgAEGp8bpzF9Hqo1QeCb9R9TWQpsrh4mPvBvQFjvll2TiMyOSY6V1IyqagMF+4fJ1u1IlhaX9Ahs2qy93e8GOgyTptLfnGTayCRz+esHoCQX8AwBOg5v2bmMJghcAVy7WPeY396UJg70CqtZGoKIbAqTHCvZeUbfVRUocyREjbj0R023051LAY8mdK8qFNLcME6uUgHy0Q2WjLB++rlxet9vnonfOiYA7W+rniDwulXEs/8Cdi6Vtke+KnVxEZHNYZJjBQUllTh6OR8AcHNHLuVANqyiBPj6UWm+l14Tga53Nv21/u2BW56Utn9cIHX1WEvBFeBsirRtytw4N6JQAMPrmSBQCOC7+YCuEuiUCHS9yzzXIyKzY5JjBanncqATQIdAD4T6cBZUsmHbXgJyzwBeocDI10x//U1/BwI6A8XZQMpL5o+vIYe+kBKzyJuBNh3Md97QXjXD5vUTBB5eC1zcKc2nM+p1Dk8msmFMcqzgt+r1qoawq4ps2YVdwO7l0vZd70rDxk3lpK6eOwfS8OvmLI9gKp2u1tw4ZmrFqW1orQkCD3wG/FRdf3PrvwC/KPNfj4jMhkmOFezi/Dhk68qLgK9nAxBA3ylApxYU7kYNBmIfkM71vyekda8s6cJvQP5FabbhbmPNf36fcOCmudL2//4uFTcHdq2ZNJCIbBaTHAu7lFeCC7klUCkVGNTeX+5wiOq3daGUKPhEAHe83PLz3fGS1BKUeVQanWRJ+lacHhMAF3fLXOPmedIEgXp3viWtdE1ENo1JjoXpW3F6R/jCy5X/KJINOrutZmbfscuatmp3YzwCgOHVSx1sfwUouNzyc9an9Brw57fSdkvnxrkRtVfN+xnwCBAZb7lrEZHZMMmxsN+46jjZsrIC4JvqrpiBs8y7WnPvB4GIQdJimT88Zb7z1nZ0gzQ3T1B3aVkFS+p9PzD/ODDy/yx7HSIyGyY5FqTTCfyuT3K4lAPZoi3PAJor0vDvhOfNe26lfqkDJ+Dkd8DJ7817fkAqBAakVhxrjHLyaSu9LyJqFfh/qwX9ma7BtZJKeLio0DvCV+5wiIyd2gIc+i8ABTBuOeDiYf5rBHcD4qtbin74F1BRbL5zpx+WFg9VuUhz+hARXUf2JGfZsmWIioqCq6sr4uLisHfv3hsev3TpUsTExMDNzQ0RERGYP38+yspkXvm4ATurW3EGtW8DZ5XsHzVRjZI8aaQQAMTPAdoNsty1bv0X4NMOKLgE7HjVfOfVz3DcZTTgzqJ+IqpL1l/edevWISkpCYsXL8aBAwcQGxuLxMREZGVl1Xv8F198gaeffhqLFy/GiRMn8PHHH2PdunV45plnrBx503DoONms7/8JFGUCATHA7Qstey0XD2nJBwBIXQZkHm/5OStLgaNfStuWmBuHiOyCrEnOkiVLMHPmTEyfPh3dunXDihUr4O7ujpUrV9Z7/O+//46bb74ZDzzwAKKionDHHXfg/vvvb7T1Rw5llVrsPZ8HABjCehyyJblngWMbAIUKGL8ccHa1/DU7JwJdxwBCK82do9O17HwnvpOKpn0igPZDzRIiEdkf2ZKciooK7N+/HwkJCTXBKJVISEhAampqva+56aabsH//fkNSc+7cOXz//fcYNWqUVWI2xf6L11BepUOwtxodgzzlDoeohuaKdN+mIxDez3rXHfEa4OIJXN4L7P0A0Gmbf66D1QXHvSezEJiIGmSGpXqbJycnB1qtFsHBwUb7g4ODcfLkyXpf88ADDyAnJweDBw+GEAJVVVV49NFHb9hdVV5ejvLycsNjjUZjnjfQCP1SDjd3DICCa9uQLSmRWhitXsfiEy4tkfDjAmDLU9L8OREDpGHm7eKkhKspxc9554HzvwJQAH0mWzxsImq9ZEtymmPHjh145ZVX8P777yMuLg5nzpzBvHnz8NJLL2HhwvrrCpKTk/HCCy9YOdKaehzOj0M2p7Q6yXGToVh34Cwg+wRw9CugvAA487N0A6Tus9BeNUlPxCDAO7TuOQ6tlu7b3wb4trNa6ETU+siW5AQEBEClUiEzM9Nof2ZmJkJCQup9zcKFC/HQQw/hkUceAQD07NkTxcXFmDVrFp599lko62m2XrBgAZKSkgyPNRoNIiIizPhO6rpWXIFjVwsAsOiYbJChJacZC3C2lMpJWvxz9FtA5jHg0h4gbbd0K7wKXD0o3fZULxTq2w5oFw9ExEkjwAI6SyuOA5ad4ZiI7IJsSY6Liwv69euHlJQUjBs3DgCg0+mQkpKCuXPn1vuakpKSOomMSqUCAAgh6n2NWq2GWq02X+BN8PvZXAgBdA72RLC3FYo6iUxRek26l6MlR0/lBIT1lm5xfwOEkIaYp+0BLu2W7jOPAflp0u3IOul1zu5AZYm0LlaXO+WLn4haBVm7q5KSkjB16lT0798fAwcOxNKlS1FcXIzp06cDAKZMmYLw8HAkJycDAMaMGYMlS5agT58+hu6qhQsXYsyYMYZkxxbs5NBxsmUludK9Lc0to1BIrTa+7YBe90r7yjTA5T9qWnsu75OWiACkgmMn6/7xQkStj6xJzsSJE5GdnY1FixYhIyMDvXv3xpYtWwzFyGlpaUYtN8899xwUCgWee+45XLlyBYGBgRgzZgxeftkMqyab0c4z2QA4dJxsVImMNTmmcPUGOg6TbgCgrZJWNb92Aeg8QtbQiKh1UIiG+nnslEajgY+PDwoKCuDtbYbVlq+TlluCW/5vO5yUChxafAc81a2qtpscwUcJUgvJxNVAV3b5EFHr0Jzfb04wYWa/Vbfi9GnnywSHbJNcQ8iJiKyMSY6Z1QwdD5Q5EqIGyDmEnIjIipjkmJFWJ/D7Wamoc3CnNjJHQ1QPnRYozZe22ZJDRHaOSY4ZHb9agPySSniqnRDb1lfucIjqKisAUF2G5ybDPDlERFbEJMeM9EPHB7VvAycVP1qyQfp6HLU3oHKWNxYiIgvjL7EZ7axer4pDx8lmGepx2IpDRPaPSY6ZlFZose+CNJMsJwEkm8WRVUTkQJjkmMkfF/JQodUhxNsVHQKbsJIykRw4soqIHAgncjGTTsGeeG50VygUCigUCrnDIaofW3KIyIEwyTGTUB83PDKkvdxhEN0YW3KIyIGwu4rIkbAlh4gcCJMcIkfClhwiciBMcogcCVtyiMiBMMkhciSl0jQHnCeHiBwBkxwiR8KWHCJyIExyiByFEKzJISKHwiSHyFFUlgBVZdI2W3KIyAEwySFyFPquKqUz4OIpbyxERFbAJIfIUZTWqsfhrNxE5ACY5BA5CkPRcRt54yAishImOUSOgkXHRORgmOQQOQpDSw7nyCEix8Akh8hRGCYCZEsOETkGk5OcqKgovPjii0hLS7NEPERkKZwIkIgcjMlJzhNPPIGNGzeiffv2GD58ONauXYvy8nJLxEZE5sSaHCJyMM1Kcg4dOoS9e/eia9euePzxxxEaGoq5c+fiwIEDloiRiMyBLTlE5GCaXZPTt29fvPPOO7h69SoWL16Mjz76CAMGDEDv3r2xcuVKCCHMGScRtRRbcojIwTg194WVlZXYtGkTPvnkE2zduhWDBg3Cww8/jMuXL+OZZ57Bzz//jC+++MKcsRJRS7Alh4gcjMlJzoEDB/DJJ59gzZo1UCqVmDJlCt566y106dLFcMz48eMxYMAAswZKRC3ElhwicjAmJzkDBgzA8OHDsXz5cowbNw7Ozs51jomOjsakSZPMEiARmYG2CigrkLbZkkNEDsLkJOfcuXOIjIy84TEeHh745JNPmh0UEZlZWX7NtquvXFEQEVmVyYXHWVlZ2LNnT539e/bswb59+8wSFBGZmb4ex9UHUDW7FI+IqFUxOcmZM2cOLl26VGf/lStXMGfOHLMERURmxnocInJAJic5f/75J/r27Vtnf58+ffDnn3+aJSgiMjOOrCIiB2RykqNWq5GZmVlnf3p6Opyc2AxOZJPYkkNEDsjkJOeOO+7AggULUFBQYNiXn5+PZ555BsOHDzdrcERkJiW50j1bcojIgZjc9PLGG2/glltuQWRkJPr06QMAOHToEIKDg/H555+bPUAiMoMStuQQkeMxOckJDw/HkSNHsHr1ahw+fBhubm6YPn067r///nrnzCEiG1DKmhwicjzNKqLx8PDArFmzzB0LEVkKC4+JyAE1u1L4zz//RFpaGioqKoz233XXXS0OiojMrPSadM/uKiJyIM2a8Xj8+PE4evQoFAqFYbVxhUIBANBqteaNkIhaji05ROSATB5dNW/ePERHRyMrKwvu7u44fvw4fv31V/Tv3x87duywQIhE1GIcQk5EDsjklpzU1FRs27YNAQEBUCqVUCqVGDx4MJKTk/H3v/8dBw8etEScRNRcQrAlh4gcksktOVqtFl5eXgCAgIAAXL16FQAQGRmJU6dOmTc6Imq5iiJAVyltsyWHiByIyUlOjx49cPjwYQBAXFwcXn/9dezatQsvvvgi2rdv36wgli1bhqioKLi6uiIuLg579+5t8NjbbrsNCoWizm306NHNujaR3dO34ji5Ai7u8sZCRGRFJic5zz33HHQ6HQDgxRdfxPnz5zFkyBB8//33eOedd0wOYN26dUhKSsLixYtx4MABxMbGIjExEVlZWfUev3HjRqSnpxtux44dg0qlwr333mvytYkcAutxiMhBKYR+eFQL5OXlwc/PzzDCyhRxcXEYMGAA3nvvPQCATqdDREQEHn/8cTz99NONvn7p0qVYtGgR0tPT4eHh0ejxGo0GPj4+KCgogLe3t8nxErU6Z1KA/94NBPcAZu+SOxoiomZpzu+3SS05lZWVcHJywrFjx4z2+/v7NyvBqaiowP79+5GQkFATkFKJhIQEpKamNukcH3/8MSZNmtRgglNeXg6NRmN0I3Iohjly/OSNg4jIykxKcpydndGuXTuzzYWTk5MDrVaL4OBgo/3BwcHIyMho9PV79+7FsWPH8MgjjzR4THJyMnx8fAy3iIiIFsdN1KpwZBUROSiTa3KeffZZPPPMM8jLy7NEPCb5+OOP0bNnTwwcOLDBY/Qrputvly5dsmKERDaANTlE5KBMnifnvffew5kzZxAWFobIyMg63UQHDhxo8rkCAgKgUqmQmZlptD8zMxMhISE3fG1xcTHWrl2LF1988YbHqdVqqNXqJsdEZHfYkkNEDsrkJGfcuHFmu7iLiwv69euHlJQUw3l1Oh1SUlIwd+7cG752/fr1KC8vx4MPPmi2eIjsEltyiMhBmZzkLF682KwBJCUlYerUqejfvz8GDhyIpUuXori4GNOnTwcATJkyBeHh4UhOTjZ63ccff4xx48ahTZs2Zo2HyO6wJYeIHFSzVyE3l4kTJyI7OxuLFi1CRkYGevfujS1bthiKkdPS0qBUGpcOnTp1Cjt37sRPP/0kR8hErQtbcojIQZk8T45SqbzhcHFbX4Wc8+SQw1naE8hPAx7eCkQ0XKRPRGTLmvP7bXJLzqZNm4weV1ZW4uDBg/j000/xwgsvmHo6IrK0Ev08OWzJISLHYnKSM3bs2Dr77rnnHnTv3h3r1q3Dww8/bJbAiMgMqiqAikJpmzU5RORgTJ4npyGDBg1CSkqKuU5HROagn+0YCsDVR9ZQiIiszSxJTmlpKd555x2Eh4eb43REZC6GomM/QKmSNxYiIiszubvq+oU4hRAoLCyEu7s7/vvf/5o1OCJqIQ4fJyIHZnKS89ZbbxklOUqlEoGBgYiLi4OfHxcAJLIpHD5ORA7M5CRn2rRpFgiDiCyCLTlE5MBMrsn55JNPsH79+jr7169fj08//dQsQRGRmbAlh4gcmMlJTnJyMgICAursDwoKwiuvvGKWoIjITNiSQ0QOzOQkJy0tDdHR0XX2R0ZGIi0tzSxBEZGZ1B5dRUTkYExOcoKCgnDkyJE6+w8fPszFMolModNZ/hr62Y7ZkkNEDsjkJOf+++/H3//+d2zfvh1arRZarRbbtm3DvHnzMGnSJEvESGR/Cq4Ab3QEfnzWstdhTQ4ROTCTR1e99NJLuHDhAoYNGwYnJ+nlOp0OU6ZMYU0OUVNd+A0oyQVO/A9IfNly12FNDhE5MJOTHBcXF6xbtw7//ve/cejQIbi5uaFnz56IjIy0RHxE9unaBelecxXQaS03GzFbcojIgZmc5Oh16tQJnTp1MmcsRI5Dn+ToKoGiLMA71PzXEKJm7Sq25BCRAzK5JmfChAl47bXX6ux//fXXce+995olKCK7p09yAKDgsmWuUa4BdFXSNltyiMgBmZzk/Prrrxg1alSd/SNHjsSvv/5qlqCI7F7tJEdjoSRHX4/j7A44u1rmGkRENszkJKeoqAguLi519js7O0Oj0ZglKCK7VlkKFKbXPLZUSw7rcYjIwZmc5PTs2RPr1q2rs3/t2rXo1q2bWYIismvXLho/tlSSYxhZxYkAicgxmVx4vHDhQtx99904e/Ysbr/9dgBASkoKvvjiC2zYsMHsARLZndpdVYDlkxy25BCRgzI5yRkzZgy+/vprvPLKK9iwYQPc3NwQGxuLbdu2wd+f/5gSNUqf5Di7A5UlQMEly1ynlHPkEJFjM7m7CgBGjx6NXbt2obi4GOfOncN9992HJ598ErGxseaOj8j+6JOciDjpvuCKZa7DlhwicnDNSnIAaZTV1KlTERYWhjfffBO33347du/ebc7YiOzTtfPSfdRg6b4kRypGNjdDSw7XlCMix2RSd1VGRgZWrVqFjz/+GBqNBvfddx/Ky8vx9ddfs+iYqKn0LTlhfQBnD6CyWGrNCeho3utwSQcicnBNbskZM2YMYmJicOTIESxduhRXr17Fu+++a8nYiOyPEDVJjn804NNW2rZEXQ6HkBORg2tyS84PP/yAv//975g9ezaXcyBqrqJMoKoMUCgBnwgpyck5ZZkRVmzJISIH1+SWnJ07d6KwsBD9+vVDXFwc3nvvPeTk5FgyNiL7k1ddj+PTFlA5Az7h0mONBYqP9etWsSWHiBxUk5OcQYMG4cMPP0R6ejr+9re/Ye3atQgLC4NOp8PWrVtRWFhoyTiJ7IO+q8ovWrr3iZDuLdFdxckAicjBmTy6ysPDAzNmzMDOnTtx9OhR/OMf/8Crr76KoKAg3HXXXZaIkch+GJKcKOneUJNj5u6qqnKpoBlgSw4ROaxmDyEHgJiYGLz++uu4fPky1qxZY66YiOyXtZIcfSuOQgW4+pj33ERErUSLkhw9lUqFcePG4dtvvzXH6Yjs142SHCHMdx3DyCo/QKEw33mJiFoRsyQ5RNRE+okA9UmOd3XhcVVZTeuLOXBkFRERkxwiq6kokYaQA9IcOQDgpAY8gqRtcxYfc44cIiImOURWk39Runf1kbqR9CxRl8OWHCIiJjlEVnN9PY6eJZIctuQQETHJIbKavOvqcfQsMVcO58ghImKSQ2Q1jbXkmHPWY852TETEJIfIaq6f7VhPv7SDWWtycqV71uQQkQNjkmMuVw8Cn40F1k+TOxKyVdasySlhTQ4RUZNXIadGCAGc2wF4hsgdCdkina5mdFVDNTmFGUBVBeDk0vLrlXJ0FRERW3LMxTdSui/KACrL5I2FbE9RhjThn0JV03Kj5x4AqNQABFB41TzXY0sOERGTHLNx9wec3aVtc69DRK2fvqvKNwJQORs/p1TWqssxQ/GxTgeU5Uvb7m1afj4iolaKSY65KBSAbztpuyBN3ljI9jRUj6Pnbcbi47J8QOikbTcOIScix8Ukx5z0SU4+kxy6TmNJjjnnytEPH3fxMk99DxFRKyV7krNs2TJERUXB1dUVcXFx2Lt37w2Pz8/Px5w5cxAaGgq1Wo3OnTvj+++/t1K0jdD/UDHJoes1muSYcYQVJwIkIgIg8+iqdevWISkpCStWrEBcXByWLl2KxMREnDp1CkFBQXWOr6iowPDhwxEUFIQNGzYgPDwcFy9ehK+vr/WDr4+hJceMM9eSfWhotmM9cyY5XNKBiAiAzEnOkiVLMHPmTEyfPh0AsGLFCmzevBkrV67E008/Xef4lStXIi8vD7///jucnaXizaioKGuGfGPsrqKGNDQRoJ5FWnKY5BCRY5Otu6qiogL79+9HQkJCTTBKJRISEpCamlrva7799lvEx8djzpw5CA4ORo8ePfDKK69Aq9U2eJ3y8nJoNBqjm8UwyaH6VBQDxVnSdmMtOeZY2oEtOUREAGRMcnJycqDVahEcHGy0Pzg4GBkZGfW+5ty5c9iwYQO0Wi2+//57LFy4EG+++Sb+/e9/N3id5ORk+Pj4GG4RERFmfR9G9ElOYbo0qRsRAFyrngTQ1Rdw863/GP3oqnINUFbQsuuxJYeICIANFB6bQqfTISgoCB988AH69euHiRMn4tlnn8WKFSsafM2CBQtQUFBguF26ZMF6GY9AwMkVgAA0nCuHql1rpB4HANSeNcO9W9plxZYcIiIAMtbkBAQEQKVSITMz02h/ZmYmQkLqXxohNDQUzs7OUKlUhn1du3ZFRkYGKioq4OJSd7isWq2GWq02b/AN0c+Vk/OX1GXl39461yXb1tjIKj2fttLw74LLQHD35l+PLTlERABkbMlxcXFBv379kJKSYtin0+mQkpKC+Pj4el9z880348yZM9DpdIZ9f/31F0JDQ+tNcGTBYeR0PX2S499A0bGeuebKYUsOEREAmburkpKS8OGHH+LTTz/FiRMnMHv2bBQXFxtGW02ZMgULFiwwHD979mzk5eVh3rx5+Ouvv7B582a88sormDNnjlxvoS4OI6frNbUlx9tMSzuUVE8GyHlyiMjByTqEfOLEicjOzsaiRYuQkZGB3r17Y8uWLYZi5LS0NCiVNXlYREQEfvzxR8yfPx+9evVCeHg45s2bh6eeekqut1AXR1jR9UzprgJYk0NEZCayJjkAMHfuXMydO7fe53bs2FFnX3x8PHbv3m3hqFqASQ7VptPVjK6yVpLDmhwiIgCtbHRVq2BYpJPdVQRpOgFtOaB0Arzb3vhYQ01OC5KcihKgqlTaZksOETk4Jjnmpk9yNFcAbaW8sZD89F1VPhGAqpGG09oTAuoanuDyhvRdVUonQO3VvHMQEdkJJjnm5hEEqFwAoTPP7LXUujW1HgcAvEIAhQoQWqAos/Hj61NSqx5HoWjeOYiI7ASTHHNTKmsNI2eXlcMzJclRqgDvMGm7uV1WpazHISLSY5JjCSw+Jr2mzHZcm6H4uJkJcglHVhER6THJsQRfTghI1Zo6EaBeS0dYsSWHiMiASY4lcIQV6ZnSXQW0PMkxTATIJIeIiEmOJfhGSvdsyXFs5UVAcba0bXKS08yidU4ESERkwCTHEgyFxxfljYPkpW/FcfMDXH2a9hpvM9XksCWHiIhJjkUY5sq5Cmir5I2F5GNqVxVgvpoctuQQETHJsQivEEDpDOiqpBlvyTEZkpwmFh0DNUlOaR5QUWz6NdmSQ0RkwCTHEpSqmh8r1uU4rua05Lj6AC7VMxU3py6HLTlERAZMcixFP4ycI6wcV3OSHIWi1vIOzeiyYksOEZEBkxxL4YSAZOpEgHo+4dK9qXU5Oi1QViBtsyWHiIhJjsUYhpFzhJVD0mlrEtymTgSo19zi49J8AELadvMz7bVERHaISY6lcP0qx1aYDmgrpNXAvcNNe22zk5zqriq1T+MrnhMROQAmOZbC7irHpq/H8W0nFaKbwqeZ9VyGehy24hARAUxyLMewtMNlQKeTNxayvrxm1uMAzZ/1mCOriIiMMMmxFK9QQKECdJVAUYbc0ZC1NWdklZ53rcJjIZr+Oo6sIiIywiTHUlRONaNk2GXleJozEaCedxgABaAtB4pzmv46tuQQERlhkmNJXKjTcbWkJcdJDXgGS9um1OWU5Er3bMkhIgLAJMeyuFCn42pJkgM0b4RVCVtyiIhqY5JjSYYRVhxG7lDKC4GS6m4mv8jmnaM5SU4pa3KIiGpjkmNJHEbumPStOG7+0lpUzWFY2sGEEVYl16qvyyHkREQAkxzL0q9fxSTHseiTHFNnOq7N0JJjQisgW3KIiIwwybEkzpXjmFpajwO0rCbHvU3zr0tEZEeY5FiSdzigUFYPBc6SOxqyFjmSHCE4hJyI6DpMcixJ5VwzsRu7rBxHS2Y71tOPzCvKBKrKGz++olhaKwtgdxURUTUmOZbmw7och9OSiQD13NsATq7StuZq48frW3FUasDZvfnXJSKyI0xyLI0jrByLTlvz37olLTkKhfHyDo2pvaSDQtH86xIR2REmOZbGJMexaK5K65UpnauXZ2gBU+pyWI9DRFQHkxxL0w8jN2UoMLVe16rrcXzbAUpVy86l7+o0tSWHiIgAMMmxPLbkOBZzjKzSM2WunFJOBEhEdD0mOZZWe2kHIeSNhSzPHBMB6pky6zFbcoiI6mCSY2nebQEogKpSoDhH7mjI0szakmNC4TFrcoiI6mCSY2lOLoBXqLTNLiv7Z9Ykp1ZNTmOtgGzJISKqg0mONRi6rC7KGwdZnjkmAtTTDyGvKALK8m98LFtyiIjqYJJjDRxh5RjKCmqSDXMkOS7uNetQNdZlxZYcIqI6mORYA0dYOYZr1S117gGA2ss85zSMsGqk+JgtOUREdTDJsQYmOY7BnPU4et5NHEbOlhwiojqY5FiDYf0qdlfZtWtmrMfRa8qsx9pKoFwjbbMlh4jIgEmONfhGSvf5aZwrx55ZoiWnKUmOfiJAKAA3X/Ndm4iolWOSYw36H6rK4ppuBbI/5pwIUK8pSY7+O+Xq0/KlJIiI7AiTHGtwdgU8Q6TtAtbl2C2LtORUd3XeaNbjUtbjEBHVxyaSnGXLliEqKgqurq6Ii4vD3r17Gzx21apVUCgURjdXV1crRttM+mHkLD62TzptzX9bS3RXaa4C2qr6jynhyCoiovrInuSsW7cOSUlJWLx4MQ4cOIDY2FgkJiYiKyurwdd4e3sjPT3dcLt4sRVMsscRVvat4DKgqwJUtWa4NgfPIEDpBAgtUJRR/zGGlpw25rsuEZEdkD3JWbJkCWbOnInp06ejW7duWLFiBdzd3bFy5coGX6NQKBASEmK4BQcHWzHiZmKSY9/0XVW+7cxbF6NUAd5h0nZDdTkcPk5EVC9Zk5yKigrs378fCQkJhn1KpRIJCQlITU1t8HVFRUWIjIxEREQExo4di+PHjzd4bHl5OTQajdFNFhxGbt8M9ThmLDrWq72GVX04ESARUb1kTXJycnKg1WrrtMQEBwcjI6P+pvmYmBisXLkS33zzDf773/9Cp9PhpptuwuXL9f8AJCcnw8fHx3CLiIgw+/toktrDyMn+WKLoWK+xEVaGlhw/81+biKgVk727ylTx8fGYMmUKevfujVtvvRUbN25EYGAg/vOf/9R7/IIFC1BQUGC4XbokU0tK7e4qzpVjf+RMcvTz5LAlh4jIiJOcFw8ICIBKpUJmZqbR/szMTISEhDTpHM7OzujTpw/OnDlT7/NqtRpqtbrFsbaY/oeqolBaUdqNf3XbFUvMdqynX42cNTlERCaRtSXHxcUF/fr1Q0pKimGfTqdDSkoK4uPjm3QOrVaLo0ePIjTUjCNaLMHFHfAIlLbZZWV/LDERoB5rcoiImkX27qqkpCR8+OGH+PTTT3HixAnMnj0bxcXFmD59OgBgypQpWLBggeH4F198ET/99BPOnTuHAwcO4MEHH8TFixfxyCOPyPUWmo4jrOxTaX5Nl5G+9sqcfBpZpJMtOURE9ZK1uwoAJk6ciOzsbCxatAgZGRno3bs3tmzZYihGTktLg1JZk4tdu3YNM2fOREZGBvz8/NCvXz/8/vvv6Natm1xvoel8IoAr+znCyt7kV8/T5BEIqD3Nf359klOWD5QXGV9DCLbkEBE1QPYkBwDmzp2LuXPn1vvcjh07jB6/9dZbeOutt6wQlQWwJcc+5VmwHgcAXL0BtQ9QXiAt7xAYU/NceaE0CSHAlhwiouvI3l3lUJjk2CdLjqzS89EXH1/XCqhvxXFyA5zdLHd9IqJWiEmONemTHC7SaV8sORGgXkPDyFmPQ0TUICY51sSWHPtklZacBpIc1uMQETWISY416YcClxVIN7IPciY5nO2YiKhBTHKsSe1Zs1I0R1jZB21VTZ2MRZOcBubKKWFLDhFRQ5jkWJthoU52WdkFzWVpdJNKDXhZcELKhmY9LmVNDhFRQ5jkWBvrcuyLoasqElBa8H8nfXeV5gqg09XsZ0sOEVGDmORYm2GEFbur7II16nEAwDsMgALQVgDF2TX72ZJDRNQgJjnWZmjJuShvHGQe1kpyVM413WG1u6zYkkNE1CAmOdbG7ir7YunZjmszdFnVSnIMLTltLH99IqJWhkmOtRmSHHZX2QVrTASoV98w8pLqhUHZXUVEVAeTHGvTj64qzZPWHaLWzVrdVUCtpR3qaclx4zw5RETXY5Jjba7egKuvtM3WnNat9Jq0Mjggja6yNMNcOdXfm6oKoKJI2mZLDhFRHUxy5MARVvZB34rjEQS4eFj+etd3V+lbcRRKaZVyIiIywiRHDiw+tg/W7KoCaiU5V6T7klpdVZaco4eIqJXiv4xy4DBy+6BPcvytUHQM1HRXFWcBlWVcnJOIqBFMcuTAEVb2wdotOW5+gJObtK25UmtxTiY5RET1YZIjB65fZR+sOUcOACgUxnU5bMkhIrohJjlyYE1O63ftIpC2W9oO7GK969ZOctiSQ0R0Q0xy5KBPckpygIpieWOh5tn2EqAtB6JvAcL6WO+6tRfq5Bw5REQ3xCRHDm6+NUN+a0/sRq3Dlf3A0fUAFMAd/5a6kayl9lw5nO2YiOiGmOTIxZd1Oa2SEMBPC6Xt2ElAaKx1r1971mPW5BAR3RCTHLlwGHnrdOp74OIuwMkVuH2h9a9vVJOTK22zJYeIqF5OcgfgsDiMvPXRVgJbF0nb8XNrWlWsydBddVlqVQLYkkNE1AC25MiFw8hbn32fALlnAI9AYPAT8sTgHSbdV5bUtAKyJYeIqF5McuTCYeStS1kBsCNZ2r5tAaD2kicOZzcpyQIAbYV0z5YcIqJ6McmRCxfpbF1+WyIV+gZ0BvpOlTcW7+u6ydiSQ0RULyY5ctEnOUWZQGWpvLHQjeWnAbuXS9vDXwJUMpey6YuPAcDZA3BSyxcLEZENY5IjFzc/wMVT2uZcObYtpXriv6ghQOdEuaOpqecCAPc28sVBRGTjmOTIRaFgXU5rcOUAcPRLAAog8WXrTvzXkNotOe6c7ZiIqCFMcuTEEVa2TQjgp+ekbTkm/mtI7SSHRcdERA1ikiMntuTYNqOJ/56TO5oaRt1VTHKIiBrCJEdOHGFlu4wm/ptj3Hoit9qTELIlh4ioQUxy5MT1q2zX/lXSxH/uAcDNT8gdjTGPIEDpLG2zJYeIqEFMcuTE7irbVHviv6ELAFdveeO5nlJZ05rDlhwiogYxyZGTb6R0X5gBVJXLGwvV2PmWtPhlQGeg7zS5o6mff3vp3itE3jiIiGwYF+iUk3sbwNldWoeo4DLQpoPcEVF+GpD6vrRtCxP/NeSOfwN//Qh0HiF3JERENstG/wV3EAqFNFIm55T048okR362NvFfQ4K7SzciImoQu6vkxhFWtsMw8R+klhJbmPiPiIiajUmO3Fh8bBuEAH5aKG33mgSE9ZY1HCIiajkmOXLjMHLbcOoH4OJO25v4j4iImo1JjtwMLTnsrpJN7Yn/Bj1Wk3gSEVGrxiRHbvph5GzJkc/+VUDuaWniv8Hz5Y6GiIjMhEmO3PTrEBVeBaoq5I3FEdn6xH9ERNRsNpHkLFu2DFFRUXB1dUVcXBz27t3bpNetXbsWCoUC48aNs2yAluQZJNWBCB2guSJ3NI7HaOK/qXJHQ0REZiR7krNu3TokJSVh8eLFOHDgAGJjY5GYmIisrKwbvu7ChQt48sknMWTIECtFaiH6uXIADiO3tvO/1Zr470VA5SxvPEREZFayJzlLlizBzJkzMX36dHTr1g0rVqyAu7s7Vq5c2eBrtFotJk+ejBdeeAHt27e3YrQWwhFW1pX9F7DmfuDTO6WJ/6Jv4czBRER2SNYkp6KiAvv370dCQoJhn1KpREJCAlJTUxt83YsvvoigoCA8/PDDjV6jvLwcGo3G6GZzOFeOdRRlA5v/Abw/CDj1PaBQAf0fBu79lBP/ERHZIVmXdcjJyYFWq0VwcLDR/uDgYJw8ebLe1+zcuRMff/wxDh061KRrJCcn44UXXmhpqJbFYeSWVVkK7H4f+O0toKJQ2td5JDD8BSAwRt7YiIjIYlrV2lWFhYV46KGH8OGHHyIgIKBJr1mwYAGSkpIMjzUaDSIibGweFB+25FiETgccWQdse6mmqDu0t7RkQ3Qrr+UiIqJGyZrkBAQEQKVSITMz02h/ZmYmQkJC6hx/9uxZXLhwAWPGjDHs0+l0AAAnJyecOnUKHToYL3KpVquhVqstEL0ZsbvK/M79Avz0HJBxRHrsEwEMWwT0uAdQyl6KRkREViBrkuPi4oJ+/fohJSXFMAxcp9MhJSUFc+fOrXN8ly5dcPToUaN9zz33HAoLC/H222/bXgtNU+mTHM0VQFsFqFpVA5ttyTopzV58+kfpsdobGJIExD0KOLvJGxsREVmV7L+mSUlJmDp1Kvr374+BAwdi6dKlKC4uxvTp0wEAU6ZMQXh4OJKTk+Hq6ooePXoYvd7X1xcA6uxvVTyDAZULoK2QJgXUJz3UdIWZ0qR+Bz6V5hxSOklFxbf+C/BoWtcmERHZF9mTnIkTJyI7OxuLFi1CRkYGevfujS1bthiKkdPS0qC09+4FpRLwaQvknQOu7Jc7mtZF6IAjXwI7lwKVxdK+LncCCS8AAR1lDY2IiOSlEEIIuYOwJo1GAx8fHxQUFMDb24am8P9sLHBuh9xRtG5hfYHEl4HIm+SOhIiIzKw5v9+yt+RQtV4TgSsHpC4rMo1PBHDb00D3u1lUTEREBkxybEXvB6QbERERmQX/7CUiIiK7xCSHiIiI7BKTHCIiIrJLTHKIiIjILjHJISIiIrvEJIeIiIjsEpMcIiIisktMcoiIiMguMckhIiIiu8Qkh4iIiOwSkxwiIiKyS0xyiIiIyC4xySEiIiK7xCSHiIiI7JKT3AFYmxACAKDRaGSOhIiIiJpK/7ut/x1vCodLcgoLCwEAERERMkdCREREpiosLISPj0+TjlUIU1IiO6DT6XD16lV4eXlBoVCY9dwajQYRERG4dOkSvL29zXpue8bPzXT8zJqHn1vz8HNrHn5uprvRZyaEQGFhIcLCwqBUNq3axuFacpRKJdq2bWvRa3h7e/ML3Qz83EzHz6x5+Lk1Dz+35uHnZrqGPrOmtuDosfCYiIiI7BKTHCIiIrJLTHLMSK1WY/HixVCr1XKH0qrwczMdP7Pm4efWPPzcmoefm+nM/Zk5XOExEREROQa25BAREZFdYpJDREREdolJDhEREdklJjlERERkl5jkmMmyZcsQFRUFV1dXxMXFYe/evXKHZNOef/55KBQKo1uXLl3kDsvm/PrrrxgzZgzCwsKgUCjw9ddfGz0vhMCiRYsQGhoKNzc3JCQk4PTp0/IEa0Ma+9ymTZtW5/s3YsQIeYK1EcnJyRgwYAC8vLwQFBSEcePG4dSpU0bHlJWVYc6cOWjTpg08PT0xYcIEZGZmyhSxbWjK53bbbbfV+b49+uijMkVsG5YvX45evXoZJv2Lj4/HDz/8YHjeXN81JjlmsG7dOiQlJWHx4sU4cOAAYmNjkZiYiKysLLlDs2ndu3dHenq64bZz5065Q7I5xcXFiI2NxbJly+p9/vXXX8c777yDFStWYM+ePfDw8EBiYiLKysqsHKltaexzA4ARI0YYff/WrFljxQhtzy+//II5c+Zg9+7d2Lp1KyorK3HHHXeguLjYcMz8+fPxv//9D+vXr8cvv/yCq1ev4u6775Yxavk15XMDgJkzZxp9315//XWZIrYNbdu2xauvvor9+/dj3759uP322zF27FgcP34cgBm/a4JabODAgWLOnDmGx1qtVoSFhYnk5GQZo7JtixcvFrGxsXKH0aoAEJs2bTI81ul0IiQkRPzf//2fYV9+fr5Qq9VizZo1MkRom67/3IQQYurUqWLs2LGyxNNaZGVlCQDil19+EUJI3y1nZ2exfv16wzEnTpwQAERqaqpcYdqc6z83IYS49dZbxbx58+QLqpXw8/MTH330kVm/a2zJaaGKigrs378fCQkJhn1KpRIJCQlITU2VMTLbd/r0aYSFhaF9+/aYPHky0tLS5A6pVTl//jwyMjKMvns+Pj6Ii4vjd68JduzYgaCgIMTExGD27NnIzc2VOySbUlBQAADw9/cHAOzfvx+VlZVG37cuXbqgXbt2/L7Vcv3nprd69WoEBASgR48eWLBgAUpKSuQIzyZptVqsXbsWxcXFiI+PN+t3zeEW6DS3nJwcaLVaBAcHG+0PDg7GyZMnZYrK9sXFxWHVqlWIiYlBeno6XnjhBQwZMgTHjh2Dl5eX3OG1ChkZGQBQ73dP/xzVb8SIEbj77rsRHR2Ns2fP4plnnsHIkSORmpoKlUold3iy0+l0eOKJJ3DzzTejR48eAKTvm4uLC3x9fY2O5fetRn2fGwA88MADiIyMRFhYGI4cOYKnnnoKp06dwsaNG2WMVn5Hjx5FfHw8ysrK4OnpiU2bNqFbt244dOiQ2b5rTHJIFiNHjjRs9+rVC3FxcYiMjMSXX36Jhx9+WMbIyBFMmjTJsN2zZ0/06tULHTp0wI4dOzBs2DAZI7MNc+bMwbFjx1gnZ6KGPrdZs2YZtnv27InQ0FAMGzYMZ8+eRYcOHawdps2IiYnBoUOHUFBQgA0bNmDq1Kn45ZdfzHoNdle1UEBAAFQqVZ2q78zMTISEhMgUVevj6+uLzp0748yZM3KH0mrov1/87rVc+/btERAQwO8fgLlz5+K7777D9u3b0bZtW8P+kJAQVFRUID8/3+h4ft8kDX1u9YmLiwMAh/++ubi4oGPHjujXrx+Sk5MRGxuLt99+26zfNSY5LeTi4oJ+/fohJSXFsE+n0yElJQXx8fEyRta6FBUV4ezZswgNDZU7lFYjOjoaISEhRt89jUaDPXv28LtnosuXLyM3N9ehv39CCMydOxebNm3Ctm3bEB0dbfR8v3794OzsbPR9O3XqFNLS0hz6+9bY51afQ4cOAYBDf9/qo9PpUF5ebt7vmnlrox3T2rVrhVqtFqtWrRJ//vmnmDVrlvD19RUZGRlyh2az/vGPf4gdO3aI8+fPi127domEhAQREBAgsrKy5A7NphQWFoqDBw+KgwcPCgBiyZIl4uDBg+LixYtCCCFeffVV4evrK7755htx5MgRMXbsWBEdHS1KS0tljlxeN/rcCgsLxZNPPilSU1PF+fPnxc8//yz69u0rOnXqJMrKyuQOXTazZ88WPj4+YseOHSI9Pd1wKykpMRzz6KOPinbt2olt27aJffv2ifj4eBEfHy9j1PJr7HM7c+aMePHFF8W+ffvE+fPnxTfffCPat28vbrnlFpkjl9fTTz8tfvnlF3H+/Hlx5MgR8fTTTwuFQiF++uknIYT5vmtMcszk3XffFe3atRMuLi5i4MCBYvfu3XKHZNMmTpwoQkNDhYuLiwgPDxcTJ04UZ86ckTssm7N9+3YBoM5t6tSpQghpGPnChQtFcHCwUKvVYtiwYeLUqVPyBm0DbvS5lZSUiDvuuEMEBgYKZ2dnERkZKWbOnOnwf5TU93kBEJ988onhmNLSUvHYY48JPz8/4e7uLsaPHy/S09PlC9oGNPa5paWliVtuuUX4+/sLtVotOnbsKP75z3+KgoICeQOX2YwZM0RkZKRwcXERgYGBYtiwYYYERwjzfdcUQgjRzJYlIiIiIpvFmhwiIiKyS0xyiIiIyC4xySEiIiK7xCSHiIiI7BKTHCIiIrJLTHKIiIjILjHJISIiIrvEJIeIHJ5CocDXX38tdxhEZGZMcohIVtOmTYNCoahzGzFihNyhEVEr5yR3AEREI0aMwCeffGK0T61WyxQNEdkLtuQQkezUajVCQkKMbn5+fgCkrqTly5dj5MiRcHNzQ/v27bFhwwaj1x89ehS333473Nzc0KZNG8yaNQtFRUVGx6xcuRLdu3eHWq1GaGgo5s6da/R8Tk4Oxo8fD3d3d3Tq1AnffvutZd80EVkckxwisnkLFy7EhAkTcPjwYUyePBmTJk3CiRMnAADFxcVITEyEn58f/vjjD6xfvx4///yzURKzfPlyzJkzB7NmzcLRo0fx7bffomPHjkbXeOGFF3DffffhyJEjGDVqFCZPnoy8vDyrvk8iMjPzrSlKRGS6qVOnCpVKJTw8PIxuL7/8shBCWuX50UcfNXpNXFycmD17thBCiA8++ED4+fmJoqIiw/ObN28WSqXSsLJ4WFiYePbZZxuMAYB47rnnDI+LiooEAPHDDz+Y7X0SkfWxJoeIZDd06FAsX77caJ+/v79hOz4+3ui5+Ph4HDp0CABw4sQJxMbGwsPDw/D8zTffDJ1Oh1OnTkGhUODq1asYNmzYDWPo1auXYdvDwwPe3t7Iyspq7lsiIhvAJIeIZOfh4VGn+8hc3NzcmnScs7Oz0WOFQgGdTmeJkIjISliTQ0Q2b/fu3XUed+3aFQDQtWtXHD58GMXFxYbnd+3aBaVSiZiYGHh5eSEqKgopKSlWjZmI5MeWHCKSXXl5OTIyMoz2OTk5ISAgAACwfv169O/fH4MHD8bq1auxd+9efPzxxwCAyZMnY/HixZg6dSqef/55ZGdn4/HHH8dDDz2E4OBgAMDzzz+PRx99FEFBQRg5ciQKCwuxa9cuPP7449Z9o0RkVUxyiEh2W7ZsQWhoqNG+mJgYnDx5EoA08mnt2rV47LHHEBoaijVr1qBbt24AAHd3d/z444+YN28eBgwYAHd3d0yYMAFLliwxnGvq1KkoKyvDW2+9hSeffBIBAQG45557rPcGiUgWCiGEkDsIIqKGKBQKbNq0CePGjZM7FCJqZViTQ0RERHaJSQ4RERHZJdbkEJFNY486ETUXW3KIiIjILjHJISIiIrvEJIeIiIjsEpMcIiIisktMcoiIiMguMckhIiIiu8Qkh4iIiOwSkxwiIiKyS0xyiIiIyC79PyZzsaG6p/W7AAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"import keras\nfrom keras.models import load_model\n\n# Load the model from the file\nmodel1 = load_model(\"model.keras\")\n\n# Evaluate the model on the test data\nresults = model1.evaluate(test_images, test_labels)\n\n# Print the results\nprint(f\"Test Loss: {results[0]}\")\nprint(f\"Test Accuracy: {results[1]}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-06T19:47:53.933472Z","iopub.execute_input":"2024-06-06T19:47:53.933771Z","iopub.status.idle":"2024-06-06T19:48:00.553798Z","shell.execute_reply.started":"2024-06-06T19:47:53.933730Z","shell.execute_reply":"2024-06-06T19:48:00.552808Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 172ms/step - accuracy: 1.0000 - loss: 0.0054\nTest Loss: 0.0037182741798460484\nTest Accuracy: 1.0\n","output_type":"stream"}]}]} \ No newline at end of file diff --git a/Fracture Detection using DL/README.md b/Fracture Detection using DL/README.md new file mode 100644 index 000000000..065f40ee7 --- /dev/null +++ b/Fracture Detection using DL/README.md @@ -0,0 +1,142 @@ +## **Fracture Detection using DL** + +### 🎯 **Goal** + +The objective of this project is to classify images of x-ray scans of bones, into 2 classes: fracture and not-fractured. + +### 🧵 **Dataset** + +The dataset consists of 3 subdirectories under the bone_fracture_binary_classification directory, train, test and val, all three with 2 subdirectories: fracture and not-fractured; train with approximately 9200 images, val with approximately 850 and test with approximately 600 images respectively. + +**Appropriate image count** + +Train images images: 9165 + +Validation images: 764 + +Test images: 443 + +### 🧾 **Description** + +The project deals with binary classification, classifying images into 2 categories: fracture and not-fractured bone x-ray scans. + +### 🧮 **What I had done!** + +To achieve our goals, the following steps were implemented: + +- Images were loaded using keras.utils and normalized to the range 0 to 1. + +- Images were scanned for appropriateness. + +- Images were resized to a fixed size of 224x224 pixels. + +- Custom and pre-trained models were used for this task. + +### 🚀 **Models Implemented** + +models used: + +- ResNet-50 +- Xception +- VGG16 +- CNN +- InceptionV3 +- DenseNet-121 +- MobileNet + +### 📚 **Libraries Needed** + +- Keras + +- Tensorflow + +- Numpy + +- Matplotlib + +### 📊 **Exploratory Data Analysis Results** + + +- #### **EDA** + +

+ + +

+ + + +- #### **DenseNet-121** + +

+ + +

+ +- #### **CNN** + +

+ + +

+ +- #### **InceptionV3** + +

+ + +

+ +- #### **VGG16** + +

+ + +

+ +- #### **MobileNet** + +

+ + +

+ +- #### **RESNET50** + +

+ + +

+ +- #### **Xception** + +

+ + +

+ +### 📈 **Performance of the Models based on the Accuracy Scores** + +#### Metrics: + +We used **Loss** and **Accuracy** as metrics. + +| Models | Validation Accuracy | Validation Loss | Test Accuracy | Test Loss | +|--------|---------------------|--------------------------| ---------------------|--------------------------| +| ResNet-50 | 38.74% | 9.8734 | 44.92% | 8.8777 | +| InceptionV3 | 61.26% | 6.1766 | 55.08% | 7.1615 | +| CNN | 99.87% | 0.0100 | 100.00% | 0.0037 | +| VGG16 | 94.63% | 0.1482 | 96.16% | 0.1142 | +| MobileNet | 99.21% | 0.0346 | 100.00% | 0.0156 | +| DenseNet-121 | 94.50% | 0.1413 | 95.03% | 0.1494 | +| Xception | 98.82% | 0.0541 | 100.00% | 0.0170 | + +### 📢 **Conclusion** + +We conclude the following: + +**CNN**, **Xception**, **DenseNet-121**, **VGG16** and **MobileNet** are all up to the task and are ideal for this. + +### ✒️ **Your Signature** + +Original Contributor: Arihant Bhandari [https://github.com/Arihant-Bhandari] \ No newline at end of file