notebook/real-fake-class3.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":"2025-06-10T06:13:35.581819Z","iopub.execute_input":"2025-06-10T06:13:35.582631Z","iopub.status.idle":"2025-06-10T06:13:35.966216Z","shell.execute_reply.started":"2025-06-10T06:13:35.582606Z","shell.execute_reply":"2025-06-10T06:13:35.965471Z"}},"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":"2025-06-10T06:13:35.967470Z","iopub.execute_input":"2025-06-10T06:13:35.967678Z","iopub.status.idle":"2025-06-10T06:13:35.977792Z","shell.execute_reply.started":"2025-06-10T06:13:35.967661Z","shell.execute_reply":"2025-06-10T06:13:35.977312Z"}},"outputs":[],"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":"2025-06-10T08:59:05.636516Z","iopub.execute_input":"2025-06-10T08:59:05.636741Z","iopub.status.idle":"2025-06-10T09:00:08.992905Z","shell.execute_reply.started":"2025-06-10T08:59:05.636724Z","shell.execute_reply":"2025-06-10T09:00:08.992227Z"}},"outputs":[{"name":"stdout","text":"Train ∩ Val: 0\nTrain ∩ Test: 0\nVal ∩ Test: 0\n","output_type":"stream"}],"execution_count":30},{"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":"2025-06-10T06:13:35.978415Z","iopub.execute_input":"2025-06-10T06:13:35.978660Z","iopub.status.idle":"2025-06-10T06:18:25.747888Z","shell.execute_reply.started":"2025-06-10T06:13:35.978643Z","shell.execute_reply":"2025-06-10T06:18:25.747351Z"}},"outputs":[{"name":"stdout","text":"Found 140002 files belonging to 2 classes.\nFound 39428 files belonging to 2 classes.\nFound 10905 files belonging to 2 classes.\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"print(\"Class names and their labels:\", train.class_names)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T09:47:11.779067Z","iopub.execute_input":"2025-06-10T09:47:11.779386Z","iopub.status.idle":"2025-06-10T09:47:11.783819Z","shell.execute_reply.started":"2025-06-10T09:47:11.779365Z","shell.execute_reply":"2025-06-10T09:47:11.783028Z"}},"outputs":[{"name":"stdout","text":"Class names and their labels: ['Fake', 'Real']\n","output_type":"stream"}],"execution_count":31},{"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(32, (3,3), padding='same', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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', kernel_regularizer=l2(1e-4)),\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":"2025-06-10T12:55:42.747929Z","iopub.execute_input":"2025-06-10T12:55:42.748536Z","iopub.status.idle":"2025-06-10T12:55:42.995385Z","shell.execute_reply.started":"2025-06-10T12:55:42.748513Z","shell.execute_reply":"2025-06-10T12:55:42.994645Z"}},"outputs":[],"execution_count":53},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T12:55:47.162915Z","iopub.execute_input":"2025-06-10T12:55:47.163193Z","iopub.status.idle":"2025-06-10T12:55:47.213087Z","shell.execute_reply.started":"2025-06-10T12:55:47.163173Z","shell.execute_reply":"2025-06-10T12:55:47.212365Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_4\"\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_4\"</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_4 (\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_4 (\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_4 (\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_4 (\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_4                 │ (\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_4 (\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_58 (\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;34m32\u001b[0m)        │             \u001b[38;5;34m896\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_66               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m128\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_66 (\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;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_58 (\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;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_59 (\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;34m18,496\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_67               │ (\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_67 (\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_59 (\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_60 (\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;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_68               │ (\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;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_68 (\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;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_60 (\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;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_61 (\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;34m73,856\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_69               │ (\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_69 (\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_61 (\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_62 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_70               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_70 (\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;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_62 (\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;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_63 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │         \u001b[38;5;34m295,168\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_71               │ (\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_71 (\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_63 (\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_64 (\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_72               │ (\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_72 (\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_64 (\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_65 (\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;34m256\u001b[0m)           │         \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_73               │ (\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;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_73 (\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;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_65 (\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;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_66 (\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;34m1,180,160\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_74               │ (\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_74 (\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_66 (\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_67 (\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_75               │ (\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_75 (\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_67 (\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_68 (\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_76               │ (\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_76 (\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_68 (\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_69 (\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_77               │ (\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_77 (\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_69 (\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_4           │ (\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_12 (\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_78               │ (\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_78 (\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_8 (\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_13 (\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_79               │ (\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_79 (\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_9 (\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_14 (\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_4 (<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_4 (<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_4 (<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_4 (<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_4                 │ (<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_4 (<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_58 (<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\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_66               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_66 (<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\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_58 (<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\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_59 (<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\">18,496</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_67               │ (<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_67 (<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_59 (<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_60 (<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\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_68               │ (<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\">256</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_68 (<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\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_60 (<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\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_61 (<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\">73,856</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_69               │ (<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_69 (<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_61 (<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_62 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_70               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_70 (<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\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_62 (<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\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_63 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_71               │ (<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_71 (<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_63 (<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_64 (<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_72               │ (<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_72 (<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_64 (<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_65 (<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\">256</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_73               │ (<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\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_73 (<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\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_65 (<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\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_66 (<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\">1,180,160</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_74               │ (<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_74 (<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_66 (<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_67 (<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_75               │ (<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_75 (<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_67 (<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_68 (<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_76               │ (<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_76 (<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_68 (<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_69 (<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_77               │ (<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_77 (<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_69 (<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_4           │ (<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_12 (<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_78               │ (<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_78 (<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_8 (<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_13 (<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_79               │ (<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_79 (<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_9 (<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_14 (<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;34m10,356,609\u001b[0m (39.51 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\">10,356,609</span> (39.51 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,348,865\u001b[0m (39.48 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,348,865</span> (39.48 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,744\u001b[0m (30.25 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\">7,744</span> (30.25 KB)\n</pre>\n"},"metadata":{}}],"execution_count":54},{"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":"2025-06-10T12:55:52.109713Z","iopub.execute_input":"2025-06-10T12:55:52.109989Z","iopub.status.idle":"2025-06-10T12:55:52.121357Z","shell.execute_reply.started":"2025-06-10T12:55:52.109970Z","shell.execute_reply":"2025-06-10T12:55:52.120639Z"}},"outputs":[],"execution_count":55},{"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":"2025-06-10T12:55:54.406791Z","iopub.execute_input":"2025-06-10T12:55:54.407364Z","iopub.status.idle":"2025-06-10T14:11:47.092868Z","shell.execute_reply.started":"2025-06-10T12:55:54.407332Z","shell.execute_reply":"2025-06-10T14:11:47.092217Z"}},"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 195ms/step - accuracy: 0.7705 - loss: 0.6370\nEpoch 1: val_accuracy improved from -inf to 0.78432, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m469s\u001b[0m 207ms/step - accuracy: 0.7706 - loss: 0.6369 - val_accuracy: 0.7843 - val_loss: 0.6968 - 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 196ms/step - accuracy: 0.9329 - loss: 0.2583\nEpoch 2: val_accuracy did not improve from 0.78432\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m454s\u001b[0m 208ms/step - accuracy: 0.9329 - loss: 0.2582 - val_accuracy: 0.7081 - val_loss: 0.7737 - 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 196ms/step - accuracy: 0.9409 - loss: 0.2296\nEpoch 3: val_accuracy improved from 0.78432 to 0.86058, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m455s\u001b[0m 208ms/step - accuracy: 0.9409 - loss: 0.2296 - val_accuracy: 0.8606 - val_loss: 0.5089 - 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 196ms/step - accuracy: 0.9461 - loss: 0.2191\nEpoch 4: val_accuracy improved from 0.86058 to 0.93233, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m454s\u001b[0m 208ms/step - accuracy: 0.9461 - loss: 0.2191 - val_accuracy: 0.9323 - val_loss: 0.3313 - 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 195ms/step - accuracy: 0.9512 - loss: 0.2071\nEpoch 5: val_accuracy did not improve from 0.93233\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m454s\u001b[0m 207ms/step - accuracy: 0.9512 - loss: 0.2071 - val_accuracy: 0.9265 - val_loss: 0.4095 - 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 196ms/step - accuracy: 0.9540 - loss: 0.1974\nEpoch 6: val_accuracy did not improve from 0.93233\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m455s\u001b[0m 208ms/step - accuracy: 0.9540 - loss: 0.1974 - val_accuracy: 0.8830 - val_loss: 0.4277 - 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 196ms/step - accuracy: 0.9562 - loss: 0.1891\nEpoch 7: val_accuracy did not improve from 0.93233\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m455s\u001b[0m 208ms/step - accuracy: 0.9562 - loss: 0.1891 - val_accuracy: 0.8152 - val_loss: 0.4933 - 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 195ms/step - accuracy: 0.9666 - loss: 0.1540\nEpoch 8: val_accuracy improved from 0.93233 to 0.94268, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m454s\u001b[0m 207ms/step - accuracy: 0.9666 - loss: 0.1540 - val_accuracy: 0.9427 - val_loss: 0.2139 - learning_rate: 5.0000e-04\nEpoch 9/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - accuracy: 0.9677 - loss: 0.1402\nEpoch 9: val_accuracy did not improve from 0.94268\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m452s\u001b[0m 206ms/step - accuracy: 0.9677 - loss: 0.1402 - val_accuracy: 0.9342 - val_loss: 0.3263 - learning_rate: 5.0000e-04\nEpoch 10/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - accuracy: 0.9699 - loss: 0.1325\nEpoch 10: val_accuracy did not improve from 0.94268\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m451s\u001b[0m 206ms/step - accuracy: 0.9699 - loss: 0.1325 - val_accuracy: 0.9076 - val_loss: 0.3544 - learning_rate: 5.0000e-04\nRestoring model weights from the end of the best epoch: 8.\n","output_type":"stream"},{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x79353e5d5bd0>"},"metadata":{}}],"execution_count":56},{"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":"2025-06-10T14:11:47.094245Z","iopub.execute_input":"2025-06-10T14:11:47.094790Z","iopub.status.idle":"2025-06-10T14:12:19.040194Z","shell.execute_reply.started":"2025-06-10T14:11:47.094772Z","shell.execute_reply":"2025-06-10T14:12:19.039486Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step \n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n\u001b[1m2/2\u001b[0m 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40ms/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 559ms/step\nTest Accuracy: 0.8898\nPrecision: 0.9010\nRecall: 0.8740\nF1 Score: 0.8873\n","output_type":"stream"}],"execution_count":57},{"cell_type":"code","source":"!zip -r folder.zip /kaggle/working/best_model.keras","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T14:12:19.043559Z","iopub.execute_input":"2025-06-10T14:12:19.043747Z","iopub.status.idle":"2025-06-10T14:12:25.545757Z","shell.execute_reply.started":"2025-06-10T14:12:19.043731Z","shell.execute_reply":"2025-06-10T14:12:25.544793Z"}},"outputs":[{"name":"stdout","text":"updating: kaggle/working/best_model.keras (deflated 8%)\n","output_type":"stream"}],"execution_count":58},{"cell_type":"code","source":"from IPython.display import FileLink\n\nFileLink(r'folder.zip')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T14:12:25.546893Z","iopub.execute_input":"2025-06-10T14:12:25.547138Z","iopub.status.idle":"2025-06-10T14:12:25.552965Z","shell.execute_reply.started":"2025-06-10T14:12:25.547116Z","shell.execute_reply":"2025-06-10T14:12:25.552267Z"}},"outputs":[{"execution_count":59,"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":59}]}