AlexNetConfig¶

class lucid.models.AlexNetConfig(num_classes: int = 1000, in_channels: int = 3, dropout: float = 0.5, classifier_hidden_features: tuple[int, int] = (4096, 4096))¶

AlexNetConfig stores the architectural choices used by lucid.models.AlexNet. It controls the output class count, the number of input channels, the dropout rate used in the classifier, and the hidden widths of the two fully connected classifier layers.

Class Signature¶

@dataclass
class AlexNetConfig:
    num_classes: int = 1000
    in_channels: int = 3
    dropout: float = 0.5
    classifier_hidden_features: tuple[int, int] = (4096, 4096)

Parameters¶

num_classes (int): Number of output classes.
in_channels (int): Number of channels in the input image tensor.
dropout (float): Dropout probability applied before the first two classifier linear layers.
classifier_hidden_features (tuple[int, int]): Hidden widths of the two classifier linear layers.

Validation¶

num_classes must be greater than 0.
in_channels must be greater than 0.
dropout must be in the range [0.0, 1.0).
classifier_hidden_features must contain exactly 2 positive integers.

Usage¶

import lucid.models as models

config = models.AlexNetConfig(
    num_classes=10,
    in_channels=1,
    dropout=0.25,
    classifier_hidden_features=(512, 256),
)

model = models.AlexNet(config)