ncalab.models.basicNCA.abstractNCArule

Classes

AbstractNCARule

Base class for all neural network modules.

Module Contents

class ncalab.models.basicNCA.abstractNCArule.AbstractNCARule(device: torch.device, input_size: int, hidden_size: int, output_size: int)

Bases: torch.nn.Module, abc.ABC

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

device

input_size

hidden_size

output_size

abstractmethod freeze(freeze_last: bool = False) → None