Keras
English
intrusion-detection
network-forensics
iot-security
cnn
lstm
multiclass-classification
cybersecurity
Instructions to use Codelord01/multiclass_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Codelord01/multiclass_model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Codelord01/multiclass_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4be00b4812079426adeb9e62dcf05ca6283b391291dd1adcb2630177744bc950
- Size of remote file:
- 459 kB
- SHA256:
- 7dd217f4d0478294fa8c1fa1496fbc8913dc28a2c402d22333d13d440c4bed8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.