minerva.models.nets.mlp
=======================

.. py:module:: minerva.models.nets.mlp


Classes
-------

.. autoapisummary::

   minerva.models.nets.mlp.MLP


Module Contents
---------------

.. py:class:: MLP(layer_sizes, activation_cls = nn.ReLU, *args, **kwargs)

   Bases: :py:obj:`torch.nn.Sequential`


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

   This MLP is composed of a sequence of linear layers interleaved with ReLU activation
   functions, except for the final layer which remains purely linear.

   Example
   -------

   >>> mlp = MLP(10, 20, 30, 40)
   >>> print(mlp)
   MLP(
       (0): Linear(in_features=10, out_features=20, bias=True)
       (1): ReLU()
       (2): Linear(in_features=20, out_features=30, bias=True)
       (3): ReLU()
       (4): Linear(in_features=30, out_features=40, bias=True)
   )

   Initializes the MLP with specified layer sizes.

   Parameters
   ----------
   layer_sizes : Sequence[int]
       A sequence of positive integers indicating the size of each layer.
       At least two integers are required, representing the input and output layers.
   activation_cls : type
       The class of the activation function to use between layers. Default is nn.ReLU.
   *args
       Additional arguments passed to the activation function.
   **kwargs
       Additional keyword arguments passed to the activation function.

   Raises
   ------
   AssertionError
       If fewer than two layer sizes are provided or if any layer size is not a positive integer.
   AssertionError
       If activation_cls does not inherit from torch.nn.Module.