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
File size: 660 Bytes
15b3eec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"tag": "CICIDS2017__CNN__FedProx__iid",
"dataset": "CICIDS2017",
"model": "CNN",
"fl_algo": "FedProx",
"partition": "iid",
"n_clients": 30,
"n_rounds": 30,
"max_cap": 2000,
"total_time_s": 6643.6,
"model_params": 8993,
"model_mb": 0.036,
"final": {
"accuracy": 0.9616143453401843,
"precision": 0.8677341414472337,
"recall": 0.9499412708802195,
"f1_binary": 0.9069787350614663,
"f1_macro": 0.9413982199065669,
"f1_weighted": 0.9622567138277615,
"fpr": 0.03552197439619902,
"auc_roc": 0.9906983037995104,
"threshold": 0.8112906217575073,
"round": 30,
"comm_mb": 0,
"round_time_s": 0
}
} |