chainerx.linear¶
-
chainerx.
linear
(x, W, b=None, n_batch_axis=1)¶ Linear function, or affine transformation.
It accepts two or three arguments: an input minibatch
x
, a weight matrixW
, and optionally a bias vectorb
. It computes\[Y = xW^\top + b.\]- Parameters
x (ndarray) – Input array, which is a \((s_1, s_2, ..., s_n)\)-shaped array.
W (ndarray) – Weight variable of shape \((M, N)\), where \((N = s_{\rm n\_batch\_axes} * ... * s_n)\).
b (ndarray) – Bias variable (optional) of shape \((M,)\).
n_batch_axes (int) – The number of batch axes. The default is 1. The input variable is reshaped into (\({\rm n\_batch\_axes} + 1\))-dimensional tensor. This should be greater than 0.
- Returns
Output array with shape of \((s_1, ..., s_{\rm n\_batch\_axes}, M)\).
- Return type
Note
During backpropagation, this function propagates the gradient of the output array to input arrays
x
,W
andb
.