| """ |
| Network Initialization Functions |
| """ |
|
|
| import torch.nn as nn |
|
|
|
|
| def init_dynamics_network(model, scale=0.01): |
| """Initialize dynamics network with small random weights""" |
| for module in model.modules(): |
| if isinstance(module, nn.Linear): |
| nn.init.normal_(module.weight, mean=0.0, std=scale) |
| if module.bias is not None: |
| nn.init.zeros_(module.bias) |
|
|
|
|
| def init_autoencoder_network(model): |
| """Initialize autoencoder network with Xavier initialization""" |
| for module in model.modules(): |
| if isinstance(module, nn.Linear): |
| nn.init.xavier_uniform_(module.weight, gain=nn.init.calculate_gain('tanh')) |
| if module.bias is not None: |
| nn.init.zeros_(module.bias) |
|
|
|
|
| def init_event_network(model, scale=0.1): |
| """ |
| Initialize event function network with large standard deviation. |
| |
| Large initialization helps prevent runaway event boundaries where the event function |
| is never triggered, which would result in no gradients and model degeneration to |
| vanilla Neural ODE. |
| |
| Args: |
| model: Event function network |
| scale: Standard deviation for weight initialization (default: 0.1, larger than dynamics) |
| """ |
| for module in model.modules(): |
| if isinstance(module, nn.Linear): |
| nn.init.normal_(module.weight, mean=0.0, std=scale) |
| if module.bias is not None: |
| |
| nn.init.normal_(module.bias, mean=0.0, std=scale * 0.5) |
|
|
|
|
| def init_reset_network(model, scale=0.05): |
| """ |
| Initialize state reset network with moderate variance. |
| |
| Reset networks should provide meaningful state perturbations without being too |
| aggressive, so we use moderate initialization variance. |
| |
| Args: |
| model: State reset network |
| scale: Standard deviation for weight initialization (default: 0.05) |
| """ |
| for module in model.modules(): |
| if isinstance(module, nn.Linear): |
| nn.init.normal_(module.weight, mean=0.0, std=scale) |
| if module.bias is not None: |
| nn.init.zeros_(module.bias) |
|
|
