ncalab.models.applications.classification.classificationNCAhead

Classes

ClassificationNCAHead

Base class for all neural network modules.

Module Contents

class ncalab.models.applications.classification.classificationNCAhead.ClassificationNCAHead(num_hidden_channels: int, num_classes: int, device: torch.device, avg_pool_size: int, hidden_size: int = 32)

Bases: ncalab.models.basicNCA.abstractNCAhead.AbstractNCAHead

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.

num_hidden_channels

num_classes

device

avg_pool_size

classifier

forward(x)

Parameters:

x (torch.Tensor) – Input tensor

Returns:

NotImplemented, subclasses are required to implement this method.

freeze(freeze_last: bool = False)

Freeze head weights.

Parameters:

freeze_last (bool, optional) – Whether to freeze the last layer (if applicable), defaults to True

Returns:

NotImplemented, subclasses are required to implement this method.