Bilinear module — nn_bilinear • torch

Applies a bilinear transformation to the incoming data \(y = x_1^T A x_2 + b\)

Usage

nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)

Arguments

in1_features: size of each first input sample
in2_features: size of each second input sample
out_features: size of each output sample
bias: If set to FALSE, the layer will not learn an additive bias. Default: TRUE

Shape

Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and \(*\) means any number of additional dimensions. All but the last dimension of the inputs should be the same.
Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).
Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\) and all but the last dimension are the same shape as the input.

Attributes

weight: the learnable weights of the module of shape \((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\). The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where \(k = \frac{1}{\mbox{in1\_features}}\)
bias: the learnable bias of the module of shape \((\mbox{out\_features})\). If bias is TRUE, the values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where \(k = \frac{1}{\mbox{in1\_features}}\)

Examples

if (torch_is_installed()) {
m <- nn_bilinear(20, 30, 50)
input1 <- torch_randn(128, 20)
input2 <- torch_randn(128, 30)
output <- m(input1, input2)
print(output$size())
}
#> [1] 128  50