chainer.functions.max_pooling_nd¶

chainer.functions.max_pooling_nd(x, ksize, stride=None, pad=0, cover_all=True, return_indices=False)[source]¶

N-dimensionally spatial max pooling function.

Warning

This feature is experimental. The interface can change in the future.

This function provides a N-dimensionally generalized version of max_pooling_2d(). This acts similarly to convolution_nd(), but it computes the maximum 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. cover_all (bool) – If `True`, all spatial locations are pooled into some output pixels. It may make the output size larger. return_indices (bool) – If `True`, pooling indices array is returned together with the output variable. The returned indices are expected for use by `chainer.functions.upsampling_nd()`. Note that cuDNN will not be used for this function if `return_indices` is set to `True`, as cuDNN does not return indices information.
Returns:	When `return_indices` is `False` (default), returns the output variable. When `False`, returns the tuple of the output variable and pooling indices (ndarray). Pooling indices will be on the same device as the input.
Return type:	Variable or tuple