multi_layer_perceptron

MultilayerPerceptron(learning_rate, output_size, hidden_dimensions=None, *, activation_function=None)

Bases: LightningModule

A multilayer perceptron.

Initialize the model.

Parameters:

Name	Type	Description	Default
learning_rate	float	The learning rate.	required
output_size	int	The size of the output.	required
hidden_dimensions	list[int] | None	The dimensions of the hidden layers.	None
activation_function	type[Module] | None	The activation function to use. Defaults to ReLU if not provided.	None

Source code in src/flowcean/torch/multi_layer_perceptron.py

def __init__(
    self,
    learning_rate: float,
    output_size: int,
    hidden_dimensions: list[int] | None = None,
    *,
    activation_function: type[torch.nn.Module] | None = None,
) -> None:
    """Initialize the model.

    Args:
        learning_rate: The learning rate.
        output_size: The size of the output.
        hidden_dimensions: The dimensions of the hidden layers.
        activation_function: The activation function to use.
            Defaults to ReLU if not provided.
    """
    super().__init__()
    if hidden_dimensions is None:
        hidden_dimensions = []
    self.save_hyperparameters()
    self.learning_rate = learning_rate

    layers: list[Module] = []
    for dimension in hidden_dimensions:
        layers.extend(
            (
                torch.nn.LazyLinear(dimension),
                activation_function()
                if activation_function
                else torch.nn.ReLU(),
            ),
        )
    layers.append(torch.nn.LazyLinear(output_size))
    self.model = torch.nn.Sequential(*layers)