CollaborativeCoding.metrics.EntropyPred

Classes

EntropyPrediction

Base class for all neural network modules.

Module Contents

class CollaborativeCoding.metrics.EntropyPred.EntropyPrediction(num_classes, macro_averaging=None)

Bases: torch.nn.Module

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.

stored_entropy_values = []

num_classes

__call__(y_true: torch.Tensor, y_logits: torch.Tensor)

Computes the Shannon Entropy of the predicted logits and stores the results. Args:

y_true: The true labels. This parameter is not used in the computation

but is included for compatibility with certain interfaces.

y_logits: The predicted logits from which entropy is calculated.

Returns:

torch.Tensor: The aggregated entropy value(s) based on the specified

method (‘mean’, ‘sum’, or ‘none’).

__returnmetric__()

__reset__()