Instructions to use merve/hyperparam_table with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use merve/hyperparam_table with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://merve/hyperparam_table") - Notebooks
- Google Colab
- Kaggle
| library_name: keras | |
| ## 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: | |
| | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | | |
| |----|-------------|-----|------|------|-------|-------|------------------| | |
| |Adam|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32| | |
| ## Training Metrics | |
| | Epochs | Train Loss | | |
| |--- |--- | | |
| | 1| 4.71| | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |