{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":45867,"databundleVersionId":6924515,"sourceType":"competition"}],"dockerImageVersionId":30579,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.models import Model\n\nimport numpy as np\n\n# Load the ResNet50 model pre-trained on ImageNet data, without the top classification layer\nmodel = ResNet50(weights='imagenet', include_top=False, pooling='avg', input_shape=(512, 512, 3))\n\nmodel.save_weights(\"resnet50.h5\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-15T05:27:47.596532Z","iopub.execute_input":"2023-11-15T05:27:47.597025Z","iopub.status.idle":"2023-11-15T05:27:51.760879Z","shell.execute_reply.started":"2023-11-15T05:27:47.596984Z","shell.execute_reply":"2023-11-15T05:27:51.759514Z"},"trusted":true},"execution_count":null,"outputs":[]}]}