speechbrain.nnet.complex_networks.c_linear module
Library implementing complex-valued linear transformation.
- Authors
Titouan Parcollet 2020
Summary
Classes:
This function implements a fully connected complex-valued linear layer: y = Wx + b. |
Reference
- class speechbrain.nnet.complex_networks.c_linear.CLinear(n_neurons, input_shape, bias=True, init_criterion='glorot', weight_init='complex')[source]
Bases:
ModuleThis function implements a fully connected complex-valued linear layer: y = Wx + b. y, W, x and b are thus complex numbers. A complex number is written as: r + xi. A tensor of complex numbers x = [batch, 32] can be understood as [batch, 0:15] = R and [batch, 16:31] = Xi. Thus the features dimension is cut in half (must be divisible by 2).
- Parameters:
n_neurons (int) – It is the number of output neurons (i.e, the dimensionality of the output). Please note that these are complex-valued neurons. If 256 neurons are specified, the output dimension will be 512.
input_shape (tuple) – Expected size of the input.
bias (bool) – if True, the additive bias b is adopted.
init_criterion (str , optional) – (glorot, he). This parameter controls the initialization criterion of the weights. It is combined with weights_init to build the initialization method of the complex-valued weights (default “glorot”).
weight_init (str, optional) – (complex, unitary). This parameter defines the initialization procedure of the complex-valued weights (default “complex”). “complex” will generate random complex-valued weights following the init_criterion and the complex polar form. “unitary” will normalize the weights to lie on the unit circle. More details in: “Deep Complex Networks”, Trabelsi C. et al.
Example
>>> inputs = torch.rand(10, 50, 40) >>> lin = CLinear(n_neurons=100, input_shape=inputs.shape) >>> output = lin(inputs) >>> output.shape torch.Size([10, 50, 200])