{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":939937,"sourceType":"datasetVersion","datasetId":501529,"isSourceIdPinned":false},{"sourceId":3134515,"sourceType":"datasetVersion","datasetId":1909705},{"sourceId":7802620,"sourceType":"datasetVersion","datasetId":4568839}],"dockerImageVersionId":31041,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import kagglehub\n\n# Download latest version\npath = kagglehub.dataset_download(\"manjilkarki/deepfake-and-real-images\")\n\nprint(\"Path to dataset files:\", path)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T06:13:35.581819Z","iopub.execute_input":"2025-06-10T06:13:35.582631Z","iopub.status.idle":"2025-06-10T06:13:35.966216Z","shell.execute_reply.started":"2025-06-10T06:13:35.582606Z","shell.execute_reply":"2025-06-10T06:13:35.965471Z"}},"outputs":[{"name":"stdout","text":"Path to dataset files: /kaggle/input/deepfake-and-real-images\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"#importing packages\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.utils import image_dataset_from_directory\nimport warnings\n\nwarnings.filterwarnings('ignore')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T06:13:35.967470Z","iopub.execute_input":"2025-06-10T06:13:35.967678Z","iopub.status.idle":"2025-06-10T06:13:35.977792Z","shell.execute_reply.started":"2025-06-10T06:13:35.967661Z","shell.execute_reply":"2025-06-10T06:13:35.977312Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"#checking for duplications\n\nimport os\ndef get_all_image_paths(folder_path):\n image_paths = []\n for root, dirs, files in os.walk(folder_path):\n for file in files:\n if file.lower().endswith(('.jpg')):\n image_paths.append(os.path.join(root, file))\n return set(image_paths)\n\ntrain_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Train')\nval_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Validation')\ntest_paths = get_all_image_paths('/kaggle/input/deepfake-and-real-images/Dataset/Test')\n\n# Intersections\nprint(\"Train ∩ Val:\", len(train_paths & val_paths))\nprint(\"Train ∩ Test:\", len(train_paths & test_paths))\nprint(\"Val ∩ Test:\", len(val_paths & test_paths))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T08:59:05.636516Z","iopub.execute_input":"2025-06-10T08:59:05.636741Z","iopub.status.idle":"2025-06-10T09:00:08.992905Z","shell.execute_reply.started":"2025-06-10T08:59:05.636724Z","shell.execute_reply":"2025-06-10T09:00:08.992227Z"}},"outputs":[{"name":"stdout","text":"Train ∩ Val: 0\nTrain ∩ Test: 0\nVal ∩ Test: 0\n","output_type":"stream"}],"execution_count":30},{"cell_type":"code","source":"#loading image data\ndef load_1stimage_data(path):\n img_size = (256, 256)\n batch_size = 64\n\n train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n path + \"/Dataset/Train\",\n label_mode=\"binary\", \n image_size=img_size,\n batch_size=batch_size,\n shuffle=True\n )\n\n val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n path + \"/Dataset/Validation\",\n label_mode=\"binary\",\n image_size=img_size,\n batch_size=batch_size,\n shuffle=True \n )\n\n test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n path + \"/Dataset/Test\",\n label_mode=\"binary\",\n image_size=img_size,\n batch_size=batch_size,\n shuffle=False \n )\n\n\n return train_ds,val_ds ,test_ds\n\ntrain,val,test=load_1stimage_data('/kaggle/input/deepfake-and-real-images')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T06:13:35.978415Z","iopub.execute_input":"2025-06-10T06:13:35.978660Z","iopub.status.idle":"2025-06-10T06:18:25.747888Z","shell.execute_reply.started":"2025-06-10T06:13:35.978643Z","shell.execute_reply":"2025-06-10T06:18:25.747351Z"}},"outputs":[{"name":"stdout","text":"Found 140002 files belonging to 2 classes.\nFound 39428 files belonging to 2 classes.\nFound 10905 files belonging to 2 classes.\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"print(\"Class names and their labels:\", train.class_names)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T09:47:11.779067Z","iopub.execute_input":"2025-06-10T09:47:11.779386Z","iopub.status.idle":"2025-06-10T09:47:11.783819Z","shell.execute_reply.started":"2025-06-10T09:47:11.779365Z","shell.execute_reply":"2025-06-10T09:47:11.783028Z"}},"outputs":[{"name":"stdout","text":"Class names and their labels: ['Fake', 'Real']\n","output_type":"stream"}],"execution_count":31},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.regularizers import l2\n\nmodel = Sequential([\n Input((256, 256, 3)),\n Rescaling(1./255),\n RandomFlip(\"horizontal\"),\n RandomRotation(0.1),\n RandomZoom(0.1),\n RandomTranslation(0.1, 0.1),\n RandomContrast(0.1),\n\n Conv2D(32, (3,3), padding='same', kernel_regularizer=l2(1e-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(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 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":"2025-06-10T09:47:14.449500Z","iopub.execute_input":"2025-06-10T09:47:14.450144Z","iopub.status.idle":"2025-06-10T09:47:14.780477Z","shell.execute_reply.started":"2025-06-10T09:47:14.450120Z","shell.execute_reply":"2025-06-10T09:47:14.779663Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T09:47:19.501784Z","iopub.execute_input":"2025-06-10T09:47:19.502258Z","iopub.status.idle":"2025-06-10T09:47:19.558798Z","shell.execute_reply.started":"2025-06-10T09:47:19.502234Z","shell.execute_reply":"2025-06-10T09:47:19.557974Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_2\"\u001b[0m\n","text/html":"Model: \"sequential_2\"\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_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_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_2 │ (\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_2 (\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_30 (\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_34 │ (\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_34 (\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_30 (\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_31 (\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_35 │ (\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_35 (\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_31 (\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_32 (\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;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_36 │ (\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;34m128\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;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\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;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\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;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_37 │ (\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_37 (\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_33 (\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_34 (\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;34m32\u001b[0m) │ \u001b[38;5;34m18,464\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\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_38 (\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;34m32\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;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\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;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_39 │ (\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;34m256\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;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\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;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\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;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_40 │ (\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_40 (\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_36 (\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_37 (\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_41 │ (\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_41 (\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_37 (\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_38 (\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_42 │ (\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_42 (\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_38 (\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_39 (\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_43 │ (\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_43 (\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_39 (\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_40 (\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_44 │ (\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_44 (\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_40 (\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_41 (\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_45 │ (\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_45 (\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_41 (\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_42 (\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_46 │ (\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_46 (\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_42 (\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_43 (\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_47 │ (\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_47 (\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_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;34m512\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;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\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;34m512\u001b[0m) │ \u001b[38;5;34m2,048\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;34m512\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;34m512\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;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\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;34m512\u001b[0m) │ \u001b[38;5;34m2,048\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;34m512\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;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_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_50 │ (\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_50 (\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_51 │ (\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_51 (\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_2 (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_2 │ (None, 256, 256, 3) │ 0 │\n│ (RandomTranslation) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ random_contrast_2 (RandomContrast) │ (None, 256, 256, 3) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_30 (Conv2D) │ (None, 256, 256, 32) │ 896 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_34 │ (None, 256, 256, 32) │ 128 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_34 (LeakyReLU) │ (None, 256, 256, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_30 (MaxPooling2D) │ (None, 128, 128, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_31 (Conv2D) │ (None, 128, 128, 32) │ 9,248 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_35 │ (None, 128, 128, 32) │ 128 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_35 (LeakyReLU) │ (None, 128, 128, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_31 (MaxPooling2D) │ (None, 64, 64, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_32 (Conv2D) │ (None, 64, 64, 32) │ 9,248 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_36 │ (None, 64, 64, 32) │ 128 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_36 (LeakyReLU) │ (None, 64, 64, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_32 (MaxPooling2D) │ (None, 32, 32, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_33 (Conv2D) │ (None, 32, 32, 64) │ 18,496 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_37 │ (None, 32, 32, 64) │ 256 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_37 (LeakyReLU) │ (None, 32, 32, 64) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_33 (MaxPooling2D) │ (None, 16, 16, 64) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_34 (Conv2D) │ (None, 16, 16, 32) │ 18,464 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_38 │ (None, 16, 16, 32) │ 128 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_38 (LeakyReLU) │ (None, 16, 16, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_34 (MaxPooling2D) │ (None, 8, 8, 32) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_35 (Conv2D) │ (None, 8, 8, 64) │ 18,496 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_39 │ (None, 8, 8, 64) │ 256 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_39 (LeakyReLU) │ (None, 8, 8, 64) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_35 (MaxPooling2D) │ (None, 4, 4, 64) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_36 (Conv2D) │ (None, 4, 4, 128) │ 73,856 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_40 │ (None, 4, 4, 128) │ 512 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_40 (LeakyReLU) │ (None, 4, 4, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_36 (MaxPooling2D) │ (None, 2, 2, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_37 (Conv2D) │ (None, 2, 2, 128) │ 147,584 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_41 │ (None, 2, 2, 128) │ 512 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_41 (LeakyReLU) │ (None, 2, 2, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_37 (MaxPooling2D) │ (None, 1, 1, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_38 (Conv2D) │ (None, 1, 1, 128) │ 147,584 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_42 │ (None, 1, 1, 128) │ 512 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_42 (LeakyReLU) │ (None, 1, 1, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_38 (MaxPooling2D) │ (None, 1, 1, 128) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_39 (Conv2D) │ (None, 1, 1, 256) │ 295,168 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_43 │ (None, 1, 1, 256) │ 1,024 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_43 (LeakyReLU) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_39 (MaxPooling2D) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_40 (Conv2D) │ (None, 1, 1, 256) │ 590,080 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_44 │ (None, 1, 1, 256) │ 1,024 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_44 (LeakyReLU) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_40 (MaxPooling2D) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_41 (Conv2D) │ (None, 1, 1, 256) │ 590,080 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_45 │ (None, 1, 1, 256) │ 1,024 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_45 (LeakyReLU) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_41 (MaxPooling2D) │ (None, 1, 1, 256) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_42 (Conv2D) │ (None, 1, 1, 512) │ 1,180,160 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_46 │ (None, 1, 1, 512) │ 2,048 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_46 (LeakyReLU) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_42 (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_43 (Conv2D) │ (None, 1, 1, 512) │ 2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_47 │ (None, 1, 1, 512) │ 2,048 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_47 (LeakyReLU) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_43 (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_44 (Conv2D) │ (None, 1, 1, 512) │ 2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_48 │ (None, 1, 1, 512) │ 2,048 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_48 (LeakyReLU) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_44 (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ conv2d_45 (Conv2D) │ (None, 1, 1, 512) │ 2,359,808 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_49 │ (None, 1, 1, 512) │ 2,048 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_49 (LeakyReLU) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ max_pooling2d_45 (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ global_average_pooling2d_2 │ (None, 512) │ 0 │\n│ (GlobalAveragePooling2D) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ dense_6 (Dense) │ (None, 512) │ 262,656 │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ batch_normalization_50 │ (None, 512) │ 2,048 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_50 (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_51 │ (None, 128) │ 512 │\n│ (BatchNormalization) │ │ │\n├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n│ leaky_re_lu_51 (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;34m10,523,617\u001b[0m (40.14 MB)\n","text/html":" Total params: 10,523,617 (40.14 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,515,425\u001b[0m (40.11 MB)\n","text/html":" Trainable params: 10,515,425 (40.11 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m8,192\u001b[0m (32.00 KB)\n","text/html":" Non-trainable params: 8,192 (32.00 KB)\n
\n"},"metadata":{}}],"execution_count":33},{"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":"2025-06-10T09:47:28.579623Z","iopub.execute_input":"2025-06-10T09:47:28.580322Z","iopub.status.idle":"2025-06-10T09:47:28.591065Z","shell.execute_reply.started":"2025-06-10T09:47:28.580272Z","shell.execute_reply":"2025-06-10T09:47:28.590443Z"}},"outputs":[],"execution_count":34},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\ncheckpoint=ModelCheckpoint(filepath='best_model.keras',monitor='val_accuracy',verbose=1,save_best_only=True,mode='max')\n\nearlystop=EarlyStopping(monitor='val_accuracy',patience=5,verbose=1,restore_best_weights=True)\n\nlr_scheduler=ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3)\n\n\nmodel.fit(train,epochs=10,validation_data=val,callbacks=[checkpoint, earlystop, lr_scheduler])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T09:48:16.667202Z","iopub.execute_input":"2025-06-10T09:48:16.668005Z","iopub.status.idle":"2025-06-10T10:51:44.738341Z","shell.execute_reply.started":"2025-06-10T09:48:16.667978Z","shell.execute_reply":"2025-06-10T10:51:44.737605Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7357 - loss: 1.0702\nEpoch 1: val_accuracy improved from -inf to 0.84651, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m383s\u001b[0m 175ms/step - accuracy: 0.7358 - loss: 1.0700 - val_accuracy: 0.8465 - val_loss: 0.6062 - learning_rate: 0.0010\nEpoch 2/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.9052 - loss: 0.3763\nEpoch 2: val_accuracy did not improve from 0.84651\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m383s\u001b[0m 175ms/step - accuracy: 0.9052 - loss: 0.3763 - val_accuracy: 0.4981 - val_loss: 0.7110 - learning_rate: 0.0010\nEpoch 3/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - accuracy: 0.9170 - loss: 0.3253\nEpoch 3: val_accuracy did not improve from 0.84651\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m377s\u001b[0m 172ms/step - accuracy: 0.9170 - loss: 0.3253 - val_accuracy: 0.4981 - val_loss: 0.7249 - learning_rate: 0.0010\nEpoch 4/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.9249 - loss: 0.3255\nEpoch 4: val_accuracy did not improve from 0.84651\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m379s\u001b[0m 173ms/step - accuracy: 0.9249 - loss: 0.3255 - val_accuracy: 0.7440 - val_loss: 8.9396 - learning_rate: 0.0010\nEpoch 5/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.9396 - loss: 0.2876\nEpoch 5: val_accuracy improved from 0.84651 to 0.91042, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m381s\u001b[0m 174ms/step - accuracy: 0.9396 - loss: 0.2876 - val_accuracy: 0.9104 - val_loss: 0.7441 - learning_rate: 5.0000e-04\nEpoch 6/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.9486 - loss: 0.2207\nEpoch 6: val_accuracy improved from 0.91042 to 0.91389, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m382s\u001b[0m 175ms/step - accuracy: 0.9486 - loss: 0.2207 - val_accuracy: 0.9139 - val_loss: 0.4001 - learning_rate: 5.0000e-04\nEpoch 7/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.9512 - loss: 0.2098\nEpoch 7: val_accuracy did not improve from 0.91389\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m381s\u001b[0m 174ms/step - accuracy: 0.9512 - loss: 0.2098 - val_accuracy: 0.8586 - val_loss: 4.5540 - learning_rate: 5.0000e-04\nEpoch 8/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.9501 - loss: 0.2052\nEpoch 8: val_accuracy did not improve from 0.91389\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m381s\u001b[0m 174ms/step - accuracy: 0.9501 - loss: 0.2052 - val_accuracy: 0.9084 - val_loss: 2.5654 - learning_rate: 5.0000e-04\nEpoch 9/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.9542 - loss: 0.1897\nEpoch 9: val_accuracy improved from 0.91389 to 0.91815, saving model to best_model.keras\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m382s\u001b[0m 174ms/step - accuracy: 0.9542 - loss: 0.1897 - val_accuracy: 0.9182 - val_loss: 0.2678 - learning_rate: 5.0000e-04\nEpoch 10/10\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.9551 - loss: 0.1875\nEpoch 10: val_accuracy did not improve from 0.91815\n\u001b[1m2188/2188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m381s\u001b[0m 174ms/step - accuracy: 0.9551 - loss: 0.1875 - val_accuracy: 0.8778 - val_loss: 8.8673 - learning_rate: 5.0000e-04\nRestoring model weights from the end of the best epoch: 9.\n","output_type":"stream"},{"execution_count":36,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}],"execution_count":36},{"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":"2025-06-10T11:21:25.386428Z","iopub.execute_input":"2025-06-10T11:21:25.386671Z","iopub.status.idle":"2025-06-10T11:21:55.466841Z","shell.execute_reply.started":"2025-06-10T11:21:25.386654Z","shell.execute_reply":"2025-06-10T11:21:55.466063Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/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 38ms/step\n\u001b[1m2/2\u001b[0m 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9%)\n","output_type":"stream"}],"execution_count":43},{"cell_type":"code","source":"from IPython.display import FileLink\n\nFileLink(r'folder.zip')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-10T11:22:02.479610Z","iopub.execute_input":"2025-06-10T11:22:02.479816Z","iopub.status.idle":"2025-06-10T11:22:02.485780Z","shell.execute_reply.started":"2025-06-10T11:22:02.479795Z","shell.execute_reply":"2025-06-10T11:22:02.485112Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/folder.zip","text/html":"folder.zip
"},"metadata":{}}],"execution_count":44}]}