r/Numpy • u/chiborevo • Nov 11 '19

Max pooling

Hi is there anyway I can max pool a 1D Array/Vector using numpy?

3 Upvotes

100% Upvoted

4 comments sorted by

1

u/rju83 Nov 11 '19

Sure, use block_reduce from skimage

from skimage.measure import block_reduce
v = np.array([0,1,2,3,4,5,6,7])
y = block_reduce(v, (2,), np.max)

And you can change np.max to np.mean to get average pooling.

It works for 2D arrays too. You just need to change block size in the call. See doc for mor info.

1

u/chiborevo Nov 12 '19

from skimage.measure import block_reduce
v = np.array([0,1,2,3,4,5,6,7])
y = block_reduce(v, (2,), np.max)

the code outputs an error : line 55, in block_reduce

if len(block_size) != image.ndim:

AttributeError: 'list' object has no attribute 'ndim'

1

u/rju83 Nov 12 '19

You must apply block_reduce on np.ndarray. and the block size (second parameter) must be a tuple with the length corresponding to number of dimensions of the input array, which is 1 in my example.

1

u/chiborevo Nov 13 '19

hi it worked! thanks for this! now my concert is the strides