Instructions to use JabaleNurAdnan/CS_Checkpoints_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JabaleNurAdnan/CS_Checkpoints_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
| { | |
| "tag": "CICIDS2017__CNN_BiLSTM__FedAvg__iid", | |
| "dataset": "CICIDS2017", | |
| "model": "CNN_BiLSTM", | |
| "fl_algo": "FedAvg", | |
| "partition": "iid", | |
| "n_clients": 30, | |
| "n_rounds": 30, | |
| "max_cap": 2000, | |
| "total_time_s": 2659.8, | |
| "model_params": 101921, | |
| "model_mb": 0.4077, | |
| "final": { | |
| "accuracy": 0.9663410162342422, | |
| "precision": 0.9096619855579675, | |
| "recall": 0.9205587784343086, | |
| "f1_binary": 0.915077943260515, | |
| "f1_macro": 0.9470444700089407, | |
| "f1_weighted": 0.9664164509338282, | |
| "fpr": 0.022427521886410628, | |
| "auc_roc": 0.9946621472295331, | |
| "threshold": 0.7841078639030457, | |
| "round": 30, | |
| "comm_mb": 0, | |
| "round_time_s": 0 | |
| } | |
| } |