# chainer.functions.average_pooling_nd¶

chainer.functions.average_pooling_nd(x, ksize, stride=None, pad=0)[source]

N-dimensionally spatial average pooling function.

This function provides a N-dimensionally generalized version of average_pooling_2d(). This acts similarly to ConvolutionND, but it computes the average of input spatial patch for each channel without any parameter instead of computing the inner products.

Parameters: x (Variable) – Input variable. ksize (int or tuple of ints) – Size of pooling window. ksize=k and ksize=(k, k, ..., k) are equivalent. stride (int or tuple of ints or None) – Stride of pooling applications. stride=s and stride=(s, s, ..., s) are equivalent. If None is specified, then it uses same stride as the pooling window size. pad (int or tuple of ints) – Spatial padding width for the input array. pad=p and pad=(p, p, ..., p) are equivalent. Output variable. Variable

Note

This function currently does not support cover_all mode as max_pooling_nd(). Average pooling runs in non-cover-all mode.