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