nn_linear: Linear module

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Description

Applies a linear transformation to the incoming data: y = xA^T + b

Usage

nn_linear(in_features, out_features, bias = TRUE)

Arguments

in_features	size of each 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

• Input: (N, *, H_in) where * means any number of additional dimensions and H_in = in_features.

• Output: (N, *, H_out) where all but the last dimension are the same shape as the input and :math:H_out = out_features.

Attributes

• weight: the learnable weights of the module of shape (out_features, in_features). The values are initialized from U(-√{k}, √{k})s, where k = \frac{1}{\mbox{in\_features}}

• bias: the learnable bias of the module of shape (\mbox{out\_features}). If bias is TRUE, the values are initialized from \mathcal{U}(-√{k}, √{k}) where k = \frac{1}{\mbox{in\_features}}

Examples

if (torch_is_installed()) {
m <- nn_linear(20, 30)
input <- torch_randn(128, 20)
output <- m(input)
print(output$size())

}

torch documentation built on Oct. 7, 2021, 9:22 a.m.