minerva.models.nets.mlp

Classes

MLP	A flexible multilayer perceptron (MLP) implemented as a subclass of nn.Sequential.

Module Contents

class minerva.models.nets.mlp.MLP(layer_sizes, activation_cls=nn.ReLU, intermediate_ops=None, final_op=None, *args, **kwargs)

Bases: torch.nn.Sequential

A flexible multilayer perceptron (MLP) implemented as a subclass of nn.Sequential.

This class allows you to quickly build an MLP with: - Custom layer sizes - Configurable activation functions - Optional intermediate operations (e.g., BatchNorm, Dropout) after each linear layer - An optional final operation (e.g., normalization, final activation)

Parameters

layer_sizesSequence[int]

A list of integers specifying the sizes of each layer. Must contain at least two values: the input and output dimensions.

activation_clstype, optional

The activation function class (must inherit from nn.Module) to use between layers. Defaults to nn.ReLU.

intermediate_opsOptional[List[Optional[nn.Module]]], optional

A list of modules (e.g., nn.BatchNorm1d, nn.Dropout) to apply after each linear layer and before the activation. Each item corresponds to one linear layer. Use None to skip an operation for that layer. Must be the same length as the number of linear layers.

final_opOptional[nn.Module], optional

A module to apply after the last layer (e.g., a final activation or normalization).

*args, **kwargs :

Additional arguments passed to the activation function constructor.

Example

>>> from torch import nn
>>> mlp = MLP(
...     [128, 256, 64, 10],
...     activation_cls=nn.ReLU,
...     intermediate_ops=[nn.BatchNorm1d(256), nn.BatchNorm1d(64), None],
...     final_op=nn.Sigmoid()
... )
>>> print(mlp)
MLP(
    (0): Linear(in_features=128, out_features=256, bias=True)
    (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Linear(in_features=256, out_features=64, bias=True)
    (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): Linear(in_features=64, out_features=10, bias=True)
    (7): Sigmoid()
)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:

• layer_sizes (Sequence[int])

• activation_cls (type)

• intermediate_ops (Optional[List[Optional[torch.nn.Module]]])

• final_op (Optional[torch.nn.Module])