chainerx.max_pool¶
-
chainerx.
max_pool
(x, ksize, stride=None, pad=0, cover_all=False)¶ Spatial max pooling function.
This acts similarly to
conv()
, but it computes the maximum of input spatial patch for each channel without any parameter instead of computing the inner products.- Parameters
x (ndarray) – Input array.
ksize (int or tuple of ints) – Size of pooling window.
ksize=k
andksize=(k, k, ..., k)
are equivalent.stride (int or tuple of ints or None) – Stride of pooling applications.
stride=s
andstride=(s, s, ..., s)
are equivalent. IfNone
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
andpad=(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.
- Returns
Output array.
- Return type
Note
During backpropagation, this function propagates the gradient of the output array to the input array
x
. This function is only differentiable up to the second order.Note
In
cuda
backend, only 2 and 3 dim arrays are supported asx
because cuDNN pooling supports 2 and 3 spatial dimensions.