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{"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":[{"sourceType":"datasetVersion","sourceId":3134515,"datasetId":1909705,"databundleVersionId":3183620,"isSourceIdPinned":false}],"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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-06-18T08:09:04.319856Z","iopub.execute_input":"2025-06-18T08:09:04.320512Z","iopub.status.idle":"2025-06-18T08:09:04.753094Z","shell.execute_reply.started":"2025-06-18T08:09:04.320488Z","shell.execute_reply":"2025-06-18T08:09:04.752501Z"}},"outputs":[{"name":"stdout","text":"Path to dataset files: /kaggle/input/deepfake-and-real-images\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.regularizers import l2\nfrom tensorflow.keras.optimizers import Adam\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing import *","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T08:09:04.754159Z","iopub.execute_input":"2025-06-18T08:09:04.754380Z","iopub.status.idle":"2025-06-18T08:09:18.650377Z","shell.execute_reply.started":"2025-06-18T08:09:04.754362Z","shell.execute_reply":"2025-06-18T08:09:18.649757Z"}},"outputs":[{"name":"stderr","text":"2025-06-18 08:09:06.279478: 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 00:00:1750234146.479200 35 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1750234146.537836 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":4},{"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":"2025-06-18T08:09:18.651009Z","iopub.execute_input":"2025-06-18T08:09:18.651431Z","iopub.status.idle":"2025-06-18T08:14:55.647409Z","shell.execute_reply.started":"2025-06-18T08:09:18.651411Z","shell.execute_reply":"2025-06-18T08:14:55.646843Z"}},"outputs":[{"name":"stdout","text":"Found 140002 files belonging to 2 classes.\n","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1750234410.138183 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":5},{"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(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(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(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 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":"2025-06-18T09:38:33.369503Z","iopub.execute_input":"2025-06-18T09:38:33.370111Z","iopub.status.idle":"2025-06-18T09:38:33.672394Z","shell.execute_reply.started":"2025-06-18T09:38:33.370087Z","shell.execute_reply":"2025-06-18T09:38:33.671558Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T09:38:36.120596Z","iopub.execute_input":"2025-06-18T09:38:36.120873Z","iopub.status.idle":"2025-06-18T09:38:36.173743Z","shell.execute_reply.started":"2025-06-18T09:38:36.120854Z","shell.execute_reply":"2025-06-18T09:38:36.173182Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential\"\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\"</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_2 (\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 │ (\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 (\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_20 (\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_22 │ (\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_22 (\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_20 (\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_21 (\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;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_23 │ (\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;34m128\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;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\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;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_22 (\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;34m18,496\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_24 │ (\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_24 (\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_22 (\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_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\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;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_23 (\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;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_26 (\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_24 (\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_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_27 │ (\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;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_27 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_25 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\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_28 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_26 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_29 │ (\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_29 (\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_27 (\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_28 (\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;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_30 │ (\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;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_30 (\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;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_28 (\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_29 (\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;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_31 │ (\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;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_31 (\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;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_29 (\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_30 (\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;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_32 │ (\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;34m1,024\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_32 (\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;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_30 (\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_31 (\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_33 │ (\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_33 (\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_31 (\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_32 (\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_34 │ (\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_34 (\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_32 (\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_33 (\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_35 │ (\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_35 (\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_33 (\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_34 (\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_36 │ (\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_36 (\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_34 (\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_35 (\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_37 │ (\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_37 (\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_35 (\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_2 │ (\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_38 │ (\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_38 (\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_39 │ (\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_39 (\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_2 (<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 │ (<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 (<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_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\">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_22 │ (<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_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\">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_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\">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_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\">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\">9,248</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_23 │ (<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\">128</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\">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│ 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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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\">18,496</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_24 │ (<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_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\">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_22 (<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_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_25 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\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\">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│ max_pooling2d_23 (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_26 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_26 (<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_24 (<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_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_27 │ (<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\">512</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_27 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_25 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</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_28 │ (<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\">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_28 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_26 (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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\">295,168</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_29 │ (<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_29 (<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_27 (<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_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_30 │ (<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\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_30 (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_28 (<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_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_31 │ (<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\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_31 (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_29 (<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_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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\">590,080</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_32 │ (<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\">1,024</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_32 (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_30 (<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_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_33 │ (<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_33 (<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_31 (<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_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_34 │ (<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_34 (<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_32 (<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_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_35 │ (<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_35 (<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_33 (<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_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_36 │ (<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_36 (<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_34 (<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_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_37 │ (<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_37 (<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_35 (<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_2 │ (<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_38 │ (<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_38 (<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_39 │ (<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_39 (<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;34m13,467,041\u001b[0m (51.37 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\">13,467,041</span> (51.37 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,457,441\u001b[0m (51.34 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\">13,457,441</span> (51.34 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m9,600\u001b[0m (37.50 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\">9,600</span> (37.50 KB)\n</pre>\n"},"metadata":{}}],"execution_count":17},{"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":"2025-06-18T09:40:45.185888Z","iopub.execute_input":"2025-06-18T09:40:45.186198Z","iopub.status.idle":"2025-06-18T09:40:45.197636Z","shell.execute_reply.started":"2025-06-18T09:40:45.186173Z","shell.execute_reply":"2025-06-18T09:40:45.197099Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\ncheckpoint=ModelCheckpoint(filepath='/kaggle/working/best_model.keras',monitor='val_accuracy',verbose=1,save_best_only=True,mode='max')\nearlystop=EarlyStopping(monitor='val_accuracy',patience=5,verbose=1,restore_best_weights=True)\nlr_scheduler=ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3)\n\nmodel.fit(train,validation_data=val,epochs=20,callbacks=[checkpoint,earlystop,lr_scheduler])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T09:40:48.282401Z","iopub.execute_input":"2025-06-18T09:40:48.283033Z","iopub.status.idle":"2025-06-18T11:28:59.974462Z","shell.execute_reply.started":"2025-06-18T09:40:48.283013Z","shell.execute_reply":"2025-06-18T11:28:59.973601Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.7196 - loss: 0.6971\nEpoch 1: val_accuracy improved from -inf to 0.78553, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m427s\u001b[0m 185ms/step - accuracy: 0.7196 - loss: 0.6970 - val_accuracy: 0.7855 - val_loss: 0.7028 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9233 - loss: 0.2815\nEpoch 2: val_accuracy improved from 0.78553 to 0.89398, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m405s\u001b[0m 185ms/step - accuracy: 0.9233 - loss: 0.2815 - val_accuracy: 0.8940 - val_loss: 0.3689 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9393 - loss: 0.2270\nEpoch 3: val_accuracy did not improve from 0.89398\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m405s\u001b[0m 185ms/step - accuracy: 0.9393 - loss: 0.2270 - val_accuracy: 0.8849 - val_loss: 0.4107 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9442 - loss: 0.2169\nEpoch 4: val_accuracy did not improve from 0.89398\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m402s\u001b[0m 184ms/step - accuracy: 0.9442 - loss: 0.2169 - val_accuracy: 0.8515 - val_loss: 0.4963 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9456 - loss: 0.2143\nEpoch 5: val_accuracy did not improve from 0.89398\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m405s\u001b[0m 185ms/step - accuracy: 0.9456 - loss: 0.2143 - val_accuracy: 0.5019 - val_loss: 0.6450 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9595 - loss: 0.1745\nEpoch 6: val_accuracy improved from 0.89398 to 0.91828, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m403s\u001b[0m 184ms/step - accuracy: 0.9595 - loss: 0.1745 - val_accuracy: 0.9183 - val_loss: 0.4436 - learning_rate: 5.0000e-04\nEpoch 7/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9623 - loss: 0.1594\nEpoch 7: val_accuracy improved from 0.91828 to 0.93885, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m403s\u001b[0m 184ms/step - accuracy: 0.9623 - loss: 0.1594 - val_accuracy: 0.9389 - val_loss: 0.2962 - learning_rate: 5.0000e-04\nEpoch 8/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9656 - loss: 0.1487\nEpoch 8: val_accuracy improved from 0.93885 to 0.94377, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m403s\u001b[0m 184ms/step - accuracy: 0.9656 - loss: 0.1487 - val_accuracy: 0.9438 - val_loss: 0.3855 - learning_rate: 5.0000e-04\nEpoch 9/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9652 - loss: 0.1444\nEpoch 9: val_accuracy did not improve from 0.94377\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m403s\u001b[0m 184ms/step - accuracy: 0.9652 - loss: 0.1444 - val_accuracy: 0.5019 - val_loss: 0.6614 - learning_rate: 5.0000e-04\nEpoch 10/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9678 - loss: 0.1351\nEpoch 10: val_accuracy did not improve from 0.94377\n\u001b[1m2188/2188\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9747 - loss: 0.1110 - val_accuracy: 0.5242 - val_loss: 0.4914 - learning_rate: 2.5000e-04\nEpoch 13/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9747 - loss: 0.1094\nEpoch 13: val_accuracy did not improve from 0.95364\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 185ms/step - accuracy: 0.9747 - loss: 0.1094 - val_accuracy: 0.9450 - val_loss: 0.3599 - learning_rate: 2.5000e-04\nEpoch 14/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9752 - loss: 0.1043\nEpoch 14: val_accuracy did not improve from 0.95364\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9752 - loss: 0.1043 - val_accuracy: 0.5019 - val_loss: 0.6402 - learning_rate: 2.5000e-04\nEpoch 15/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9780 - loss: 0.0969\nEpoch 15: val_accuracy did not improve from 0.95364\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 184ms/step - accuracy: 0.9780 - loss: 0.0969 - val_accuracy: 0.9434 - val_loss: 0.5124 - learning_rate: 1.2500e-04\nEpoch 16/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9793 - loss: 0.0899\nEpoch 16: val_accuracy did not improve from 0.95364\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9793 - loss: 0.0899 - val_accuracy: 0.6995 - val_loss: 0.4838 - learning_rate: 1.2500e-04\nEpoch 16: early stopping\nRestoring model weights from the end of the best epoch: 11.\n","output_type":"stream"},{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7e308ee72110>"},"metadata":{}}],"execution_count":21},{"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":"2025-06-18T11:59:09.197504Z","iopub.execute_input":"2025-06-18T11:59:09.197790Z","iopub.status.idle":"2025-06-18T11:59:43.431276Z","shell.execute_reply.started":"2025-06-18T11:59:09.197770Z","shell.execute_reply":"2025-06-18T11:59:43.430560Z"}},"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 37ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n\u001b[1m2/2\u001b[0m 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38ms/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994ms/step\nTest Accuracy: 0.8676\nPrecision: 0.9302\nRecall: 0.7927\nF1 Score: 0.8560\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"!zip -r folder.zip /kaggle/working/best_model.keras","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:59:43.437084Z","iopub.execute_input":"2025-06-18T11:59:43.437335Z","iopub.status.idle":"2025-06-18T11:59:51.888149Z","shell.execute_reply.started":"2025-06-18T11:59:43.437317Z","shell.execute_reply":"2025-06-18T11:59:51.887381Z"}},"outputs":[{"name":"stdout","text":" adding: kaggle/working/best_model.keras (deflated 8%)\n","output_type":"stream"}],"execution_count":24},{"cell_type":"code","source":"from IPython.display import FileLink\n\nFileLink(r'folder.zip')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:59:51.889392Z","iopub.execute_input":"2025-06-18T11:59:51.889647Z","iopub.status.idle":"2025-06-18T11:59:51.895789Z","shell.execute_reply.started":"2025-06-18T11:59:51.889623Z","shell.execute_reply":"2025-06-18T11:59:51.895161Z"}},"outputs":[{"execution_count":25,"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":25}]} |