{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Overview\n\nThe goal of this notebook is to write TFRecord files to easily load and train a model.\n\nImage Pre-Processing - [RSNA-ATD | Pre-Processing 📝](https://www.kaggle.com/code/davidjohnmillard/rsna-atd-pre-processing?scriptVersionId=139731341)\n\nPNG|Dataset - [RSNA-ATD | preprocessed](https://www.kaggle.com/datasets/davidjohnmillard/rsna-atd-preprocessed)\n\nTFRecord|Dataset - **(WORK IN PROGRESS)**\n\n# Imports/Setup\n\nImporting necessary libraries and setting up base dataframes.","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport os\nimport tensorflow_io as tfio","metadata":{"execution":{"iopub.status.busy":"2023-08-12T21:09:49.549282Z","iopub.execute_input":"2023-08-12T21:09:49.549701Z","iopub.status.idle":"2023-08-12T21:09:58.139973Z","shell.execute_reply.started":"2023-08-12T21:09:49.549667Z","shell.execute_reply":"2023-08-12T21:09:58.139108Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection/image_level_labels.csv')\nlabels = pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection/train.csv')","metadata":{"execution":{"iopub.status.busy":"2023-08-12T21:11:11.041447Z","iopub.execute_input":"2023-08-12T21:11:11.041853Z","iopub.status.idle":"2023-08-12T21:11:11.082214Z","shell.execute_reply.started":"2023-08-12T21:11:11.041823Z","shell.execute_reply":"2023-08-12T21:11:11.081311Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train","metadata":{"execution":{"iopub.status.busy":"2023-08-12T21:11:56.581142Z","iopub.execute_input":"2023-08-12T21:11:56.581824Z","iopub.status.idle":"2023-08-12T21:11:56.593322Z","shell.execute_reply.started":"2023-08-12T21:11:56.581793Z","shell.execute_reply":"2023-08-12T21:11:56.592431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels","metadata":{"execution":{"iopub.status.busy":"2023-08-12T21:13:14.905885Z","iopub.execute_input":"2023-08-12T21:13:14.906283Z","iopub.status.idle":"2023-08-12T21:13:14.926075Z","shell.execute_reply.started":"2023-08-12T21:13:14.906251Z","shell.execute_reply":"2023-08-12T21:13:14.924977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Next things on agenda.\n\n1. Setup the the labels as per instance.\n\n2. Seperate file directories for minority classes.\n\n# TO BE CONTINUED","metadata":{}}]}