core.nn.enets.LinearProjectionRB

core.nn.enets.LinearProjectionRB(input_dims, n_rb, V_rb_list)

A compression layer that reduces the input dimensionality via projection onto a reduced basis. The input data is of shape (batch_size, num_blocks, num_channels, num_bins). Each of the num_blocks blocks (for GW use case: block=detector) is treated independently.

A single block consists of 1D data with num_bins bins (e.g. GW use case: num_bins=number of frequency bins). It has num_channels>=2 different channels, channel 0 and 1 store the real and imaginary part of the signal. Channels with index >=2 are used for auxiliary signals (such as PSD for GW use case).

This layer compresses the complex signal in channels 0 and 1 to n_rb reduced-basis (rb) components. This is achieved by initializing the weights of this layer with the rb matrix V, such that the (2*n_rb) dimensional output of each block is the concatenation of the real and imaginary part of the reduced basis projection of the complex signal in channel 0 and 1. The projection of the auxiliary channels with index >=2 onto these components is initialized with 0.

Module specs

input dimension:    (batch_size, num_blocks, num_channels, num_bins)
output dimension:   (batch_size, 2 * n_rb * num_blocks)

Parameters

Name	Type	Description	Default
input_dims	list	dimensions of input batch, omitting batch dimension input_dims = [num_blocks, num_channels, num_bins]	required
n_rb	int	number of reduced basis elements used for projection the output dimension of the layer is 2 * n_rb * num_blocks	required
V_rb_list	tuple of np.arrays, or None	tuple with V matrices of the reduced basis SVD projection, convention for SVD matrix decomposition: U @ s @ V^h; if None, layer is not initialized with reduced basis projection, this is useful when loading a saved model	required

Attributes

Name	Description
input_dim
input_dims
layers_rb
n_rb
output_dim

Methods

Name	Description
forward	RB projection. Additional kwargs (like context) are ignored.
init_layers	Loop through layers and initialize them individually with the
test_dimensions	Test if input dimensions to this layer are consistent with each

forward

core.nn.enets.LinearProjectionRB.forward(x, **_)

RB projection. Additional kwargs (like context) are ignored.

init_layers

core.nn.enets.LinearProjectionRB.init_layers(V_rb_list)

Loop through layers and initialize them individually with the corresponding rb projection. V_rb_list is a list that contains the rb matrix V for each block. Each matrix V in V_rb_list is represented with a numpy array of shape (self.num_bins, num_el), where num_el >= self.n_rb.

test_dimensions

core.nn.enets.LinearProjectionRB.test_dimensions(V_rb_list)

Test if input dimensions to this layer are consistent with each other, and the reduced basis matrices V.