FeedForward#

class FeedForward(input_dimensions, output_dimensions, inner_size=20, n_layers=2, func=<class 'torch.nn.modules.activation.Tanh'>, layers=None, bias=True)[source]#

Bases: Module

Feed Forward neural network model class, also known as Multi-layer Perceptron.

Initialization of the FeedForward class.

Parameters:

  • input_dimensions (int) – The number of input components. The expected tensor shape is \((*, d)\), where * represents any number of preceding dimensions (including none), and \(d\) corresponds to input_dimensions.

  • output_dimensions (int) – The number of output components . The expected tensor shape is \((*, d)\), where * represents any number of preceding dimensions (including none), and \(d\) corresponds to output_dimensions.

  • inner_size (int) – The number of neurons for each hidden layer. Default is 20.

  • n_layers (int) – The number of hidden layers. Default is 2.

  • func (torch.nn.Module | list[torch.nn.Module]) – The activation function. If a list is passed, it must have the same length as n_layers. If a single function is passed, it is used for all layers, except for the last one. Default is torch.nn.Tanh.

  • layers (list[int]) – The list of the dimension of inner layers. If None, n_layers of dimension inner_size are used. Otherwise, it overrides the values passed to n_layers and inner_size. Default is None.

  • bias (bool) – If True bias is considered for the basis function neural network. Default is True.

Raises:

  • ValueError – If the input dimension is not an integer.

  • ValueError – If the output dimension is not an integer.

  • RuntimeError – If the number of layers and functions are inconsistent.

forward(x)[source]#

Forward pass for the FeedForward model.

Parameters:

x (torch.Tensor | LabelTensor) – The input tensor.

Returns:

The output tensor.

Return type:

torch.Tensor | LabelTensor