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tensorflowdeep-learningmax-pooling

Understanding Tensorflow maxpooling

I am unable to understand why tensorflow maxpooling with my parameters.

When performed a maxpool with ksize=2 and strides=2 i get the following output with both padding SAME and padding VALID

input :  (?, 28, 28, 1)
conv2d_out :  (?, 28, 28, 32)
maxpool2d_out :  (?, 14, 14, 32)

But when I try to perfrom maxpool with ksize=3 and strides=1, I get the following output:

input :  (?, 28, 28, 1)
conv2d_out :  (?, 28, 28, 32)
maxpool2d_out :  (?, 28, 28, 32) PADDING SAME
maxpool2d_out :  (?, 26, 26, 32) PADDING VALID

maxpool with ksize=2 and strides=2 using padding SAME should have produced the output maxpool2d_out : (?, 28, 28, 32)

Is there something I missed out on how max pooling with padding works?

**CODE**==python_

Solution

  • I saw in your code you use padding=SAME. When using the SAME paddding and strides=1, the input and output size are the same. Why do you think the tensorflow implementation is wrong?

    Update: According to tensorflow documentation:

    Using SAME padding

    out_height = ceil(float(in_height) / float(strides[1]))
out_width  = ceil(float(in_width) / float(strides[2]))
    • It is 28/1=28 when k=3 and stride=1
    • It is 28/2= 14 when k=2 and stride =2

    Using VALID padding

    out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))
out_width  = ceil(float(in_width - filter_width + 1) / float(strides[2]))

    • It is celing((28-3+1)/1)= 26 when k=3, stride =1

    • It is ceiling((28-2+1)/2)= 14 when k=2, stride=2

    As you can see, because of the ceiling function, your result happens to be the same using different PADDING configuration