CollaborativeCoding.models.magnus_model

Attributes

image_shape

Classes

MagnusModel

Base class for all neural network modules.

Module Contents

class CollaborativeCoding.models.magnus_model.MagnusModel(image_shape, num_classes: int)

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.

layer1

layer2

layer3

forward(x)

Defines the forward pass of the MagnusModel. Args:

x (torch.Tensor): A four-dimensional tensor with shape

(Batch Size, Channels, Image Height, Image Width).

Returns:

torch.Tensor: The output tensor containing class logits for each input sample.

CollaborativeCoding.models.magnus_model.image_shape = (3, 28, 28)