Instructions to use wlaminack/GradientBoostedTreesModeltest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use wlaminack/GradientBoostedTreesModeltest with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://wlaminack/GradientBoostedTreesModeltest") - Notebooks
- Google Colab
- Kaggle
| library_name: keras | |
| tags: | |
| - object-detection | |
| - newtag | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| | Hyperparameters | Value | | |
| | :-- | :-- | | |
| | name | RMSprop | | |
| | weight_decay | None | | |
| | clipnorm | None | | |
| | global_clipnorm | None | | |
| | clipvalue | None | | |
| | use_ema | False | | |
| | ema_momentum | 0.99 | | |
| | ema_overwrite_frequency | 100 | | |
| | jit_compile | False | | |
| | is_legacy_optimizer | False | | |
| | learning_rate | 0.0010000000474974513 | | |
| | rho | 0.9 | | |
| | momentum | 0.0 | | |
| | epsilon | 1e-07 | | |
| | centered | False | | |
| | training_precision | float32 | | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |