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: 585 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 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"name": "UNSW_NB15",
"n_train": 206138,
"n_test": 51535,
"n_features": 30,
"features": [
"sbytes",
"id",
"smean",
"sload",
"dbytes",
"ct_state_ttl",
"sttl",
"dttl",
"rate",
"dur",
"dmean",
"dinpkt",
"dload",
"dpkts",
"sinpkt",
"tcprtt",
"synack",
"ackdat",
"sjit",
"spkts",
"ct_dst_sport_ltm",
"djit",
"dloss",
"sloss",
"ct_srv_dst",
"ct_src_dport_ltm",
"ct_dst_ltm",
"ct_srv_src",
"ct_dst_src_ltm",
"ct_src_ltm"
],
"n_clients": 30,
"alpha": 0.5
} |