{"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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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-3)),\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":"2025-06-18T12:05:06.382939Z","iopub.execute_input":"2025-06-18T12:05:06.383276Z","iopub.status.idle":"2025-06-18T12:05:06.722090Z","shell.execute_reply.started":"2025-06-18T12:05:06.383254Z","shell.execute_reply":"2025-06-18T12:05:06.721557Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n warnings.warn(\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T12:05:22.782436Z","iopub.execute_input":"2025-06-18T12:05:22.782725Z","iopub.status.idle":"2025-06-18T12:05:22.843040Z","shell.execute_reply.started":"2025-06-18T12:05:22.782705Z","shell.execute_reply":"2025-06-18T12:05:22.842418Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_1\"\u001b[0m\n","text/html":"
Model: \"sequential_1\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ rescaling_3 (\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_2 (\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_2 (\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_2 (\u001b[38;5;33mRandomZoom\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_translation_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mRandomTranslation\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_contrast_1 (\u001b[38;5;33mRandomContrast\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_40 │ (\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_40 (\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_36 (\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_37 (\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_41 │ (\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_41 (\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_37 (\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_38 (\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_42 │ (\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_42 (\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_38 (\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_39 (\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_43 │ (\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_43 (\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_39 (\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_40 (\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;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_44 │ (\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;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_44 (\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;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_40 (\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;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_41 (\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;34m73,856\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_45 │ (\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_45 (\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_41 (\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_42 (\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_46 │ (\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_46 (\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_42 (\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_43 (\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;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_47 │ (\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;34m512\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_47 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_43 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_44 (\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;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_48 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\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_48 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_44 (\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;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_45 (\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;34m295,168\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_49 │ (\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_49 (\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_45 (\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_46 (\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_50 │ (\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_50 (\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_46 (\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_47 (\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_51 │ (\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_51 (\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_47 (\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_48 (\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_52 │ (\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_52 (\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_48 (\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_49 (\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_53 │ (\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_53 (\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_49 (\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_50 (\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_54 │ (\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_54 (\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_50 (\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_51 (\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_55 │ (\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_55 (\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_51 (\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_52 (\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_56 │ (\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_56 (\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_52 (\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_53 (\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_57 │ (\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_57 (\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_53 (\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_3 │ (\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_6 (\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_58 │ (\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_58 (\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_4 (\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_7 (\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_59 │ (\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_59 (\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_5 (\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_8 (\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":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n│ rescaling_3 (Rescaling)              │ (None, 256, 256, 3)         │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_flip_2 (RandomFlip)           │ (None, 256, 256, 3)         │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_rotation_2 (RandomRotation)   │ (None, 256, 256, 3)         │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_zoom_2 (RandomZoom)           │ (None, 256, 256, 3)         │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_translation_1                 │ (None, 256, 256, 3)         │               0 │\n│ (RandomTranslation)                  │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_contrast_1 (RandomContrast)   │ (None, 256, 256, 3)         │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_36 (Conv2D)                   │ (None, 256, 256, 32)        │             896 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_40               │ (None, 256, 256, 32)        │             128 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_40 (LeakyReLU)           │ (None, 256, 256, 32)        │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_36 (MaxPooling2D)      │ (None, 128, 128, 32)        │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_37 (Conv2D)                   │ (None, 128, 128, 32)        │           9,248 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_41               │ (None, 128, 128, 32)        │             128 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_41 (LeakyReLU)           │ (None, 128, 128, 32)        │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_37 (MaxPooling2D)      │ (None, 64, 64, 32)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_38 (Conv2D)                   │ (None, 64, 64, 64)          │          18,496 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_42               │ (None, 64, 64, 64)          │             256 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_42 (LeakyReLU)           │ (None, 64, 64, 64)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_38 (MaxPooling2D)      │ (None, 32, 32, 64)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_39 (Conv2D)                   │ (None, 32, 32, 64)          │          36,928 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_43               │ (None, 32, 32, 64)          │             256 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_43 (LeakyReLU)           │ (None, 32, 32, 64)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_39 (MaxPooling2D)      │ (None, 16, 16, 64)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_40 (Conv2D)                   │ (None, 16, 16, 64)          │          36,928 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_44               │ (None, 16, 16, 64)          │             256 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_44 (LeakyReLU)           │ (None, 16, 16, 64)          │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_40 (MaxPooling2D)      │ (None, 8, 8, 64)            │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_41 (Conv2D)                   │ (None, 8, 8, 128)           │          73,856 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_45               │ (None, 8, 8, 128)           │             512 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_45 (LeakyReLU)           │ (None, 8, 8, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_41 (MaxPooling2D)      │ (None, 4, 4, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_42 (Conv2D)                   │ (None, 4, 4, 128)           │         147,584 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_46               │ (None, 4, 4, 128)           │             512 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_46 (LeakyReLU)           │ (None, 4, 4, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_42 (MaxPooling2D)      │ (None, 2, 2, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_43 (Conv2D)                   │ (None, 2, 2, 128)           │         147,584 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_47               │ (None, 2, 2, 128)           │             512 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_47 (LeakyReLU)           │ (None, 2, 2, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_43 (MaxPooling2D)      │ (None, 1, 1, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_44 (Conv2D)                   │ (None, 1, 1, 128)           │         147,584 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_48               │ (None, 1, 1, 128)           │             512 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_48 (LeakyReLU)           │ (None, 1, 1, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_44 (MaxPooling2D)      │ (None, 1, 1, 128)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_45 (Conv2D)                   │ (None, 1, 1, 256)           │         295,168 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_49               │ (None, 1, 1, 256)           │           1,024 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_49 (LeakyReLU)           │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_45 (MaxPooling2D)      │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_46 (Conv2D)                   │ (None, 1, 1, 256)           │         590,080 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_50               │ (None, 1, 1, 256)           │           1,024 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_50 (LeakyReLU)           │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_46 (MaxPooling2D)      │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_47 (Conv2D)                   │ (None, 1, 1, 256)           │         590,080 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_51               │ (None, 1, 1, 256)           │           1,024 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_51 (LeakyReLU)           │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_47 (MaxPooling2D)      │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_48 (Conv2D)                   │ (None, 1, 1, 256)           │         590,080 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_52               │ (None, 1, 1, 256)           │           1,024 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_52 (LeakyReLU)           │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_48 (MaxPooling2D)      │ (None, 1, 1, 256)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_49 (Conv2D)                   │ (None, 1, 1, 512)           │       1,180,160 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_53               │ (None, 1, 1, 512)           │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_53 (LeakyReLU)           │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_49 (MaxPooling2D)      │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_50 (Conv2D)                   │ (None, 1, 1, 512)           │       2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_54               │ (None, 1, 1, 512)           │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_54 (LeakyReLU)           │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_50 (MaxPooling2D)      │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_51 (Conv2D)                   │ (None, 1, 1, 512)           │       2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_55               │ (None, 1, 1, 512)           │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_55 (LeakyReLU)           │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_51 (MaxPooling2D)      │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_52 (Conv2D)                   │ (None, 1, 1, 512)           │       2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_56               │ (None, 1, 1, 512)           │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_56 (LeakyReLU)           │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_52 (MaxPooling2D)      │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_53 (Conv2D)                   │ (None, 1, 1, 512)           │       2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_57               │ (None, 1, 1, 512)           │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_57 (LeakyReLU)           │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_53 (MaxPooling2D)      │ (None, 1, 1, 512)           │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ global_average_pooling2d_3           │ (None, 512)                 │               0 │\n│ (GlobalAveragePooling2D)             │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_6 (Dense)                      │ (None, 512)                 │         262,656 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_58               │ (None, 512)                 │           2,048 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_58 (LeakyReLU)           │ (None, 512)                 │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_4 (Dropout)                  │ (None, 512)                 │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_7 (Dense)                      │ (None, 128)                 │          65,664 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_59               │ (None, 128)                 │             512 │\n│ (BatchNormalization)                 │                             │                 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_59 (LeakyReLU)           │ (None, 128)                 │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dropout_5 (Dropout)                  │ (None, 128)                 │               0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_8 (Dense)                      │ (None, 1)                   │             129 │\n└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m13,652,321\u001b[0m (52.08 MB)\n","text/html":"
 Total params: 13,652,321 (52.08 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,642,337\u001b[0m (52.04 MB)\n","text/html":"
 Trainable params: 13,642,337 (52.04 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m9,984\u001b[0m (39.00 KB)\n","text/html":"
 Non-trainable params: 9,984 (39.00 KB)\n
\n"},"metadata":{}}],"execution_count":27},{"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":"2025-06-18T12:05:30.134497Z","iopub.execute_input":"2025-06-18T12:05:30.134776Z","iopub.status.idle":"2025-06-18T12:05:30.146067Z","shell.execute_reply.started":"2025-06-18T12:05:30.134757Z","shell.execute_reply":"2025-06-18T12:05:30.145450Z"}},"outputs":[],"execution_count":28},{"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":"2025-06-18T12:05:35.049360Z","iopub.execute_input":"2025-06-18T12:05:35.049897Z","iopub.status.idle":"2025-06-18T14:21:20.023010Z","shell.execute_reply.started":"2025-06-18T12:05:35.049876Z","shell.execute_reply":"2025-06-18T14:21:20.022277Z"}},"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 175ms/step - accuracy: 0.7016 - loss: 1.3781\nEpoch 1: val_accuracy improved from -inf to 0.70173, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m431s\u001b[0m 187ms/step - accuracy: 0.7016 - loss: 1.3779 - val_accuracy: 0.7017 - val_loss: 0.7165 - 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 175ms/step - accuracy: 0.9007 - loss: 0.4119\nEpoch 2: val_accuracy improved from 0.70173 to 0.85936, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m408s\u001b[0m 186ms/step - accuracy: 0.9007 - loss: 0.4119 - val_accuracy: 0.8594 - val_loss: 0.6196 - 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 175ms/step - accuracy: 0.9106 - loss: 0.3774\nEpoch 3: val_accuracy did not improve from 0.85936\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m407s\u001b[0m 186ms/step - accuracy: 0.9106 - loss: 0.3774 - val_accuracy: 0.4981 - val_loss: 0.7928 - 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 175ms/step - accuracy: 0.9183 - loss: 0.3551\nEpoch 4: val_accuracy did not improve from 0.85936\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 186ms/step - accuracy: 0.9183 - loss: 0.3551 - val_accuracy: 0.5019 - val_loss: 0.8869 - 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 175ms/step - accuracy: 0.8599 - loss: 0.4806\nEpoch 5: val_accuracy improved from 0.85936 to 0.88064, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m407s\u001b[0m 186ms/step - accuracy: 0.8600 - loss: 0.4805 - val_accuracy: 0.8806 - val_loss: 1.7740 - 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 175ms/step - accuracy: 0.9407 - loss: 0.2645\nEpoch 6: val_accuracy improved from 0.88064 to 0.90329, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m407s\u001b[0m 186ms/step - accuracy: 0.9407 - loss: 0.2645 - val_accuracy: 0.9033 - val_loss: 0.4395 - 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 175ms/step - accuracy: 0.9434 - loss: 0.2360\nEpoch 7: val_accuracy did not improve from 0.90329\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9434 - loss: 0.2360 - val_accuracy: 0.8601 - val_loss: 0.5215 - 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 175ms/step - accuracy: 0.9501 - loss: 0.2188\nEpoch 8: val_accuracy improved from 0.90329 to 0.91199, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m409s\u001b[0m 187ms/step - accuracy: 0.9501 - loss: 0.2188 - val_accuracy: 0.9120 - val_loss: 2.7136 - 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 175ms/step - accuracy: 0.9527 - loss: 0.2077\nEpoch 9: val_accuracy did not improve from 0.91199\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m405s\u001b[0m 185ms/step - accuracy: 0.9527 - loss: 0.2077 - val_accuracy: 0.4981 - val_loss: 0.6511 - 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: 0.9612 - loss: 0.1750\nEpoch 10: val_accuracy improved from 0.91199 to 0.91369, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m409s\u001b[0m 187ms/step - accuracy: 0.9612 - loss: 0.1750 - val_accuracy: 0.9137 - val_loss: 0.4772 - learning_rate: 2.5000e-04\nEpoch 11/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9635 - loss: 0.1627\nEpoch 11: val_accuracy improved from 0.91369 to 0.94134, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m407s\u001b[0m 186ms/step - accuracy: 0.9635 - loss: 0.1627 - val_accuracy: 0.9413 - val_loss: 2.1506 - learning_rate: 2.5000e-04\nEpoch 12/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9644 - loss: 0.1543\nEpoch 12: val_accuracy did not improve from 0.94134\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m405s\u001b[0m 185ms/step - accuracy: 0.9644 - loss: 0.1543 - val_accuracy: 0.9405 - val_loss: 0.6834 - 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 175ms/step - accuracy: 0.9687 - loss: 0.1382\nEpoch 13: val_accuracy did not improve from 0.94134\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9687 - loss: 0.1382 - val_accuracy: 0.9249 - val_loss: 14.8721 - learning_rate: 1.2500e-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: 0.9707 - loss: 0.1278\nEpoch 14: val_accuracy did not improve from 0.94134\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 185ms/step - accuracy: 0.9707 - loss: 0.1278 - val_accuracy: 0.9396 - val_loss: 2.6018 - learning_rate: 1.2500e-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: 0.9719 - loss: 0.1228\nEpoch 15: val_accuracy improved from 0.94134 to 0.94709, 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: 0.9719 - loss: 0.1228 - val_accuracy: 0.9471 - val_loss: 2.9444 - 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 174ms/step - accuracy: 0.9737 - loss: 0.1148\nEpoch 16: val_accuracy did not improve from 0.94709\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 185ms/step - accuracy: 0.9737 - loss: 0.1148 - val_accuracy: 0.9471 - val_loss: 0.2202 - learning_rate: 6.2500e-05\nEpoch 17/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9755 - loss: 0.1083\nEpoch 17: val_accuracy improved from 0.94709 to 0.95660, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 185ms/step - accuracy: 0.9755 - loss: 0.1083 - val_accuracy: 0.9566 - val_loss: 0.5966 - learning_rate: 6.2500e-05\nEpoch 18/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9759 - loss: 0.1063\nEpoch 18: val_accuracy did not improve from 0.95660\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 185ms/step - accuracy: 0.9759 - loss: 0.1063 - val_accuracy: 0.9531 - val_loss: 0.2202 - learning_rate: 6.2500e-05\nEpoch 19/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.9760 - loss: 0.1042\nEpoch 19: val_accuracy did not improve from 0.95660\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 186ms/step - accuracy: 0.9760 - loss: 0.1042 - val_accuracy: 0.9482 - val_loss: 2.2078 - learning_rate: 6.2500e-05\nEpoch 20/20\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.9772 - loss: 0.0977\nEpoch 20: val_accuracy improved from 0.95660 to 0.95683, saving model to /kaggle/working/best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m404s\u001b[0m 185ms/step - accuracy: 0.9772 - loss: 0.0977 - val_accuracy: 0.9568 - val_loss: 0.4135 - learning_rate: 3.1250e-05\nRestoring model weights from the end of the best epoch: 20.\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}],"execution_count":29},{"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":"2025-06-18T14:21:20.024478Z","iopub.execute_input":"2025-06-18T14:21:20.024828Z","iopub.status.idle":"2025-06-18T14:21:50.491557Z","shell.execute_reply.started":"2025-06-18T14:21:20.024807Z","shell.execute_reply":"2025-06-18T14:21:50.490699Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\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 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FileLink\n\nFileLink(r'folder.zip')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T14:21:59.488628Z","iopub.execute_input":"2025-06-18T14:21:59.488874Z","iopub.status.idle":"2025-06-18T14:21:59.494686Z","shell.execute_reply.started":"2025-06-18T14:21:59.488850Z","shell.execute_reply":"2025-06-18T14:21:59.494155Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/folder.zip","text/html":"folder.zip
"},"metadata":{}}],"execution_count":32}]}