notebook/real-fake-class4.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":939937,"sourceType":"datasetVersion","datasetId":501529,"isSourceIdPinned":false},{"sourceId":3134515,"sourceType":"datasetVersion","datasetId":1909705},{"sourceId":7802620,"sourceType":"datasetVersion","datasetId":4568839}],"dockerImageVersionId":31041,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import kagglehub\n\n# Download latest version\npath = kagglehub.dataset_download(\"manjilkarki/deepfake-and-real-images\")\n\nprint(\"Path to dataset files:\", path)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T15:31:18.406836Z","iopub.execute_input":"2025-06-10T15:31:18.407522Z","iopub.status.idle":"2025-06-10T15:31:18.680240Z","shell.execute_reply.started":"2025-06-10T15:31:18.407496Z","shell.execute_reply":"2025-06-10T15:31:18.679609Z"}},"outputs":[{"name":"stdout","text":"Path to dataset files: /kaggle/input/deepfake-and-real-images\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"#importing packages\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.utils import image_dataset_from_directory\nimport warnings\n\nwarnings.filterwarnings('ignore')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T15:31:18.681454Z","iopub.execute_input":"2025-06-10T15:31:18.682038Z","iopub.status.idle":"2025-06-10T15:31:32.173469Z","shell.execute_reply.started":"2025-06-10T15:31:18.682017Z","shell.execute_reply":"2025-06-10T15:31:32.172917Z"}},"outputs":[{"name":"stderr","text":"2025-06-10 15:31:20.202191: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1749569480.391755      35 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1749569480.448653      35 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"#checking for duplications\n\nimport os\ndef get_all_image_paths(folder_path):\n    image_paths = []\n    for root, dirs, files in os.walk(folder_path):\n        for file in files:\n            if file.lower().endswith(('.jpg')):\n                image_paths.append(os.path.join(root, file))\n    return set(image_paths)\n\ntrain_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Train')\nval_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Validation')\ntest_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Test')\n\n# Intersections\nprint(\"Train ∩ Val:\", len(train_paths & val_paths))\nprint(\"Train ∩ Test:\", len(train_paths & test_paths))\nprint(\"Val ∩ Test:\", len(val_paths & test_paths))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T16:23:57.798536Z","iopub.execute_input":"2025-06-10T16:23:57.799200Z","iopub.status.idle":"2025-06-10T16:26:17.217938Z","shell.execute_reply.started":"2025-06-10T16:23:57.799179Z","shell.execute_reply":"2025-06-10T16:26:17.217160Z"}},"outputs":[{"name":"stdout","text":"Train ∩ Val: 0\nTrain ∩ Test: 0\nVal ∩ Test: 0\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"#loading image data\ndef load_1stimage_data(path):\n    img_size = (256, 256)\n    batch_size = 64\n\n    train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n        path + \"/Dataset/Train\",\n        label_mode=\"binary\",  \n        image_size=img_size,\n        batch_size=batch_size,\n        shuffle=True\n    )\n\n    val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n        path + \"/Dataset/Validation\",\n        label_mode=\"binary\",\n        image_size=img_size,\n        batch_size=batch_size,\n        shuffle=True  \n    )\n\n    test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n        path + \"/Dataset/Test\",\n        label_mode=\"binary\",\n        image_size=img_size,\n        batch_size=batch_size,\n        shuffle=False  \n    )\n\n\n    return train_ds,val_ds ,test_ds\n\ntrain,val,test=load_1stimage_data('/kaggle/input/deepfake-and-real-images')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T16:28:24.592250Z","iopub.execute_input":"2025-06-10T16:28:24.592937Z","iopub.status.idle":"2025-06-10T16:29:18.886089Z","shell.execute_reply.started":"2025-06-10T16:28:24.592913Z","shell.execute_reply":"2025-06-10T16:29:18.885334Z"}},"outputs":[{"name":"stdout","text":"Found 140002 files belonging to 2 classes.\n","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1749572943.979407      35 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15513 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n","output_type":"stream"},{"name":"stdout","text":"Found 39428 files belonging to 2 classes.\nFound 10905 files belonging to 2 classes.\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"print(\"Class names and their labels:\", train.class_names)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T16:30:06.705924Z","iopub.execute_input":"2025-06-10T16:30:06.706463Z","iopub.status.idle":"2025-06-10T16:30:06.710490Z","shell.execute_reply.started":"2025-06-10T16:30:06.706442Z","shell.execute_reply":"2025-06-10T16:30:06.709625Z"}},"outputs":[{"name":"stdout","text":"Class names and their labels: ['Fake', 'Real']\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.regularizers import l2\n\nmodel = Sequential([\n    Input((256, 256, 3)),\n    Rescaling(1./255),\n    RandomFlip(\"horizontal\"),\n    RandomRotation(0.1),\n    RandomZoom(0.1),\n    RandomTranslation(0.1, 0.1),\n    RandomContrast(0.1),\n\n    Conv2D(64, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(64, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(128, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(128, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(256, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(256, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(256, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(512, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(512, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(512, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    Conv2D(512, (3,3), padding='same'),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),\n\n    GlobalAveragePooling2D(),\n\n    Dense(512, kernel_regularizer=l2(1e-4)),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    Dropout(0.2),\n\n    Dense(128, kernel_regularizer=l2(1e-4)),\n    BatchNormalization(),\n    LeakyReLU(alpha=0.1),\n    Dropout(0.2),\n\n    Dense(1, activation='sigmoid')\n])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T17:15:43.398429Z","iopub.execute_input":"2025-06-10T17:15:43.398875Z","iopub.status.idle":"2025-06-10T17:15:43.615631Z","shell.execute_reply.started":"2025-06-10T17:15:43.398854Z","shell.execute_reply":"2025-06-10T17:15:43.614965Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T17:15:46.119845Z","iopub.execute_input":"2025-06-10T17:15:46.120110Z","iopub.status.idle":"2025-06-10T17:15:46.163687Z","shell.execute_reply.started":"2025-06-10T17:15:46.120090Z","shell.execute_reply":"2025-06-10T17:15:46.162771Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_1\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_flip_1 (\u001b[38;5;33mRandomFlip\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_rotation_1 (\u001b[38;5;33mRandomRotation\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_zoom_1 (\u001b[38;5;33mRandomZoom\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_translation_1                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mRandomTranslation\u001b[0m)                  │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_contrast_1 (\u001b[38;5;33mRandomContrast\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_13               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_13 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_14               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_14 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_15               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_15 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_16               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_16 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_17               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_17 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │         \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_18               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │           \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_18 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │         \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_19               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │           \u001b[38;5;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_19 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m1,180,160\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_20               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_20 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_18 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_21               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_21 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_19 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_22               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_22 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_20 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m2,359,808\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_23               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_23 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_21 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ global_average_pooling2d_1           │ (\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_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │         \u001b[38;5;34m262,656\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_24               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │           \u001b[38;5;34m2,048\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_24 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m65,664\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_25               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │             \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_25 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\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;34m129\u001b[0m │\n└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ rescaling_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_flip_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomFlip</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_rotation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomRotation</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_zoom_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomZoom</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_translation_1                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomTranslation</span>)                  │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_contrast_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomContrast</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_13               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_14               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_15               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_16               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_17               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_18               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_19               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_20               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_21               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_22               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_23               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ global_average_pooling2d_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)             │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,656</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_24               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_25               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,338,881\u001b[0m (39.44 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,338,881</span> (39.44 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,331,201\u001b[0m (39.41 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,331,201</span> (39.41 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,680\u001b[0m (30.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,680</span> (30.00 KB)\n</pre>\n"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"from tensorflow.keras.optimizers import Adam\n\ncustom_adam = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999)\nmodel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T17:15:50.238234Z","iopub.execute_input":"2025-06-10T17:15:50.238509Z","iopub.status.idle":"2025-06-10T17:15:50.249244Z","shell.execute_reply.started":"2025-06-10T17:15:50.238490Z","shell.execute_reply":"2025-06-10T17:15:50.248643Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\ncheckpoint=ModelCheckpoint(filepath='best_model.keras',monitor='val_accuracy',verbose=1,save_best_only=True,mode='max')\n\nearlystop=EarlyStopping(monitor='val_accuracy',patience=5,verbose=1,restore_best_weights=True)\n\nlr_scheduler=ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3)\n\n\nmodel.fit(train,epochs=10,validation_data=val,callbacks=[checkpoint, earlystop, lr_scheduler])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T17:15:53.149962Z","iopub.execute_input":"2025-06-10T17:15:53.150642Z","iopub.status.idle":"2025-06-10T19:06:30.237815Z","shell.execute_reply.started":"2025-06-10T17:15:53.150618Z","shell.execute_reply":"2025-06-10T19:06:30.236921Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.7538 - loss: 0.5324\nEpoch 1: val_accuracy improved from -inf to 0.74475, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m677s\u001b[0m 303ms/step - accuracy: 0.7538 - loss: 0.5323 - val_accuracy: 0.7447 - val_loss: 0.8141 - learning_rate: 0.0010\nEpoch 2/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9366 - loss: 0.1668\nEpoch 2: val_accuracy improved from 0.74475 to 0.87382, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m663s\u001b[0m 303ms/step - accuracy: 0.9366 - loss: 0.1668 - val_accuracy: 0.8738 - val_loss: 0.3413 - learning_rate: 0.0010\nEpoch 3/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9527 - loss: 0.1249\nEpoch 3: val_accuracy improved from 0.87382 to 0.89269, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 303ms/step - accuracy: 0.9527 - loss: 0.1249 - val_accuracy: 0.8927 - val_loss: 0.2429 - learning_rate: 0.0010\nEpoch 4/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - accuracy: 0.9585 - loss: 0.1079\nEpoch 4: val_accuracy improved from 0.89269 to 0.89830, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 303ms/step - accuracy: 0.9585 - loss: 0.1079 - val_accuracy: 0.8983 - val_loss: 0.2671 - learning_rate: 0.0010\nEpoch 5/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9650 - loss: 0.0939\nEpoch 5: val_accuracy improved from 0.89830 to 0.95742, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 303ms/step - accuracy: 0.9650 - loss: 0.0939 - val_accuracy: 0.9574 - val_loss: 0.1177 - learning_rate: 0.0010\nEpoch 6/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9678 - loss: 0.0861\nEpoch 6: val_accuracy did not improve from 0.95742\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 302ms/step - accuracy: 0.9678 - loss: 0.0861 - val_accuracy: 0.9510 - val_loss: 0.1269 - learning_rate: 0.0010\nEpoch 7/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9697 - loss: 0.0804\nEpoch 7: val_accuracy did not improve from 0.95742\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 302ms/step - accuracy: 0.9697 - loss: 0.0804 - val_accuracy: 0.9564 - val_loss: 0.1140 - learning_rate: 0.0010\nEpoch 8/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9719 - loss: 0.0746\nEpoch 8: val_accuracy did not improve from 0.95742\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 302ms/step - accuracy: 0.9719 - loss: 0.0746 - val_accuracy: 0.9483 - val_loss: 0.1405 - learning_rate: 0.0010\nEpoch 9/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9734 - loss: 0.0703\nEpoch 9: val_accuracy did not improve from 0.95742\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m662s\u001b[0m 302ms/step - accuracy: 0.9734 - loss: 0.0703 - val_accuracy: 0.9557 - val_loss: 0.1131 - learning_rate: 0.0010\nEpoch 10/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - accuracy: 0.9756 - loss: 0.0659\nEpoch 10: val_accuracy did not improve from 0.95742\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m664s\u001b[0m 303ms/step - accuracy: 0.9756 - loss: 0.0659 - val_accuracy: 0.9411 - val_loss: 0.1474 - learning_rate: 0.0010\nEpoch 10: early stopping\nRestoring model weights from the end of the best epoch: 5.\n","output_type":"stream"},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7afba43359d0>"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"from sklearn.metrics import precision_score, recall_score, f1_score\nimport numpy as np\n\ny_true = []\ny_pred = []\n\nfor images, labels in test:\n    preds = model.predict(images)\n    y_true.extend(labels.numpy())\n    y_pred.extend((preds > 0.5).astype(\"int32\").flatten())  \n\ny_true = np.array(y_true)\ny_pred = np.array(y_pred)\n\ntest_loss, test_accuracy = model.evaluate(test, verbose=0)\nprint(f\"Test Accuracy: {test_accuracy:.4f}\")\nprint(f\"Precision: {precision_score(y_true, y_pred):.4f}\")\nprint(f\"Recall: {recall_score(y_true, y_pred):.4f}\")\nprint(f\"F1 Score: {f1_score(y_true, y_pred):.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T19:06:30.239324Z","iopub.execute_input":"2025-06-10T19:06:30.239647Z","iopub.status.idle":"2025-06-10T19:07:11.881086Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n\u001b[1m2/2\u001b[0m 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Accuracy: 0.8621\nPrecision: 0.9265\nRecall: 0.7844\nF1 Score: 0.8495\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"!zip -r folder.zip /kaggle/working/best_model.keras","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T19:07:11.887226Z","iopub.execute_input":"2025-06-10T19:07:11.887488Z","iopub.status.idle":"2025-06-10T19:07:16.315987Z"}},"outputs":[{"name":"stdout","text":"  adding: kaggle/working/best_model.keras (deflated 45%)\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"from IPython.display import FileLink\n\nFileLink(r'folder.zip')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T19:07:16.317211Z","iopub.execute_input":"2025-06-10T19:07:16.317463Z","iopub.status.idle":"2025-06-10T19:07:16.323595Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/folder.zip","text/html":"<a href='folder.zip' target='_blank'>folder.zip</a><br>"},"metadata":{}}],"execution_count":null}]}