speechbrain.nnet.linear module

Library implementing linear transformation.

Authors

  • Mirco Ravanelli 2020

  • Davide Borra 2021

Summary

Classes:

Linear

Computes a linear transformation y = wx + b.

LinearWithConstraint

Computes a linear transformation y = wx + b with kernel max-norm constaint.

Reference

class speechbrain.nnet.linear.Linear(n_neurons, input_shape=None, input_size=None, bias=True, combine_dims=False)[source]

Bases: Module

Computes a linear transformation y = wx + b.

Parameters:

  • n_neurons (int) – It is the number of output neurons (i.e, the dimensionality of the output).

  • input_shape (tuple) – It is the shape of the input tensor.

  • input_size (int) – Size of the input tensor.

  • bias (bool) – If True, the additive bias b is adopted.

  • combine_dims (bool) – If True and the input is 4D, combine 3rd and 4th dimensions of input.

Example

>>> inputs = torch.rand(10, 50, 40)
>>> lin_t = Linear(input_shape=(10, 50, 40), n_neurons=100)
>>> output = lin_t(inputs)
>>> output.shape
torch.Size([10, 50, 100])
forward(x)[source]

Returns the linear transformation of input tensor.

Parameters:

x (torch.Tensor) – Input to transform linearly.

training: bool
class speechbrain.nnet.linear.LinearWithConstraint(*args, max_norm=1, **kwargs)[source]

Bases: Linear

Computes a linear transformation y = wx + b with kernel max-norm constaint. This corresponds to set an upper bound for the kernel norm.

Parameters:

  • n_neurons (int) – It is the number of output neurons (i.e, the dimensionality of the output).

  • input_shape (tuple) – It is the shape of the input tensor.

  • input_size (int) – Size of the input tensor.

  • bias (bool) – If True, the additive bias b is adopted.

  • combine_dims (bool) – If True and the input is 4D, combine 3rd and 4th dimensions of input.

  • max_norm (float) – Kernel max-norm

Example

>>> inputs = torch.rand(100,)
>>> max_norm = 1.
>>> lin_t_contrained = LinearWithConstraint(input_size=inputs.shape[0], n_neurons=2, max_norm=max_norm)
>>> output = lin_t_contrained(inputs)
>>> torch.any(torch.norm(lin_t_contrained.w.weight.data, p=2, dim=0)>max_norm)
tensor(False)
forward(x)[source]

Returns the linear transformation of input tensor.

Parameters:

x (torch.Tensor) – Input to transform linearly.

training: bool