InceptionResNet¶

ConvNet Image Classification

class lucid.models.InceptionResNet(num_classes: int)¶

Overview¶

The InceptionResNet base class defines a flexible architecture that combines Inception-style modules with residual connections. This approach improves optimization and gradient flow in deep neural networks, making it suitable for a variety of image classification tasks.

This class serves as a foundation for specific versions like Inception-ResNet v1 and v2 by providing essential components such as a stem network, convolutional layers, and fully connected layers.

Class Signature¶

class InceptionResNet(nn.Module):
    def __init__(self, num_classes: int) -> None

Parameters¶

num_classes (int): The number of output classes for the final classification layer.

Attributes¶

stem (nn.Module): The initial stem module that extracts low-level features from the input.
conv (nn.Sequential): A sequential container for the main convolutional and residual blocks.
fc (nn.Sequential): A sequential container for the fully connected layers that perform classification.

Methods¶

forward(x: Tensor) -> Tensor Performs the forward pass through the stem, convolutional blocks, and fully connected layers.

def forward(self, x):
    x = self.stem(x)
    x = self.conv(x)
    x = x.view(x.shape[0], -1)  # Flatten
    x = self.fc(x)
    return x