# chainer.functions.bias¶

chainer.functions.bias(x, y, axis=1)[source]

Computes a elementwise summation of two input variables, with the shape of the latter variable broadcasted to match the shape of the former. axis is the first axis of the first variable along which the second variable is applied.

The term “broadcasting” here comes from Caffe’s bias layer so the “broadcasting” with the following arguments:

   x : 100 x 3 x 40 x 5 x 6
y : 3 x 40
axis : 1


is equivalent to the following numpy broadcasting:

x : 100 x  3 x 40 x 5 x 6
y :  (1 x) 3 x 40 x 1 x 1


Note that the axis of x to which we apply y is specified by the argument axis, whose meaning is different from numpy’s axis.

Parameters: x (Variable or N-dimensional array) – Input variable to be summed. y (Variable or N-dimensional array) – Input variable to sum, broadcasted. axis (int) – The first axis of x along which y is applied. Output variable. Variable