Instructions to use NextGenC/MissionologyEvoNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use NextGenC/MissionologyEvoNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://NextGenC/MissionologyEvoNet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "output_base_dir": "./my_neuroevolution_results", | |
| "seq_length": 10, | |
| "train_samples": 5000, | |
| "test_samples": 1000, | |
| "pop_size": 80, | |
| "generations": 100, | |
| "mutation_rate": 0.5, | |
| "weight_mut_rate": 0.8, | |
| "activation_mut_rate": 0.2, | |
| "mutation_strength": 0.1, | |
| "tournament_size": 5, | |
| "elitism_count": 2, | |
| "batch_size": 64, | |
| "epochs_final_train": 100, | |
| "seed": 123 | |
| } |