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.

  • 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.

Return type:



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