Instructions to use Invictus-AI/self-evolving-nn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Invictus-AI/self-evolving-nn with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Invictus-AI/self-evolving-nn") - Notebooks
- Google Colab
- Kaggle
File size: 1,035 Bytes
0b3d549 | 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"config": {
"num_layers": 6,
"layers": [
{
"units": 192,
"activation": "swish",
"dropout": 0.37,
"batch_norm": true
},
{
"units": 96,
"activation": "selu",
"dropout": 0.43,
"batch_norm": true
},
{
"units": 32,
"activation": "swish",
"dropout": 0.5,
"batch_norm": true
},
{
"units": 192,
"activation": "linear",
"dropout": 0.31,
"batch_norm": true
},
{
"units": 256,
"activation": "relu",
"dropout": 0.07,
"batch_norm": true
},
{
"units": 64,
"activation": "sigmoid",
"dropout": 0.5,
"batch_norm": false
}
],
"learning_rate": 0.005,
"optimizer": "rmsprop",
"batch_size": 16,
"top3_features": [
4,
9,
16
],
"task_type": "regression"
},
"fitness": 331118.82463783136,
"generation_born": 5,
"id": "4524d43d"
} |