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

The PINA implementation of feedforward network, also refered as multilayer perceptron.

Parameters:

  • input_dimensions (int) – The number of input components of the model. Expected tensor shape of the form \((*, d)\), where * means any number of dimensions including none, and \(d\) the input_dimensions.

  • output_dimensions (int) – The number of output components of the model. Expected tensor shape of the form \((*, d)\), where * means any number of dimensions including none, and \(d\) the output_dimensions.

  • inner_size (int) – number of neurons in the hidden layer(s). Default is 20.

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

  • func (torch.nn.Module) – the activation function to use. If a single torch.nn.Module is passed, this is used as activation function after any layers, except the last one. If a list of Modules is passed, they are used as activation functions at any layers, in order.

  • layers (list(int) | tuple(int)) – a list containing the number of neurons for any hidden layers. If specified, the parameters n_layers e inner_size are not considered.

  • bias (bool) – If True the MLP will consider some bias.

forward(x)[source]#

Defines the computation performed at every call.

Parameters:

x (torch.Tensor) – The tensor to apply the forward pass.

Returns:

the output computed by the model.

Return type:

torch.Tensor