ncalab.models.basicNCA.basicNCArule

Classes

BasicNCARule

NCA rule module based on a two-layer Multi-Layer-Perceptron (MLP).

Module Contents

class ncalab.models.basicNCA.basicNCArule.BasicNCARule(device: torch.device, input_size: int, hidden_size: int, output_size: int, nonlinearity: type[torch.nn.Module] = nn.ReLU)

Bases: torch.nn.Module

NCA rule module based on a two-layer Multi-Layer-Perceptron (MLP).

Parameters:

nn (_type_) – _description_

_summary_

Parameters:

• device (torch.device) – _description_

• input_size (int) – _description_

• hidden_size (int) – _description_

• output_size (int) – _description_

• nonlinearity (type[nn.Module], optional) – _description_, defaults to nn.ReLU

nonlinearity

input_size

hidden_size

output_size

device

_build_network()

_initialize_network()

Initialize network weights of the MLP.

We assume that the default initialization of the first layer is good enough. Since the final layer is purely linear and unbiased, we initalize with 0.

forward(x: torch.Tensor) → torch.Tensor

Parameters:

x (torch.Tensor) – BCWH perception vector

Returns:

BCWH residual update

Return type:

torch.Tensor

freeze(freeze_last: bool = False)

Freeze the first layer of the NCA rule network and, optionally, the final layer.

Parameters:

freeze_last (bool, optional) – _description_, defaults to False