Linear module
Applies a linear transformation to the incoming data: y = xA^T + b
nn_linear(in_features, out_features, bias = TRUE)
in_features |
size of each input sample |
out_features |
size of each output sample |
bias |
If set to |
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.
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}}
if (torch_is_installed()) {
m <- nn_linear(20, 30)
input <- torch_randn(128, 20)
output <- m(input)
print(output$size())
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.