Tensorflow output from stride

Question

While trying to use Tensorflow I encountered a little problem regarding the stride.

I have an image of size 67*67, and I want to apply a filter of size 7*7 with stride 3. The output layer should have an edge length of 20 calculated from:

Where n is the output layer edge length (in this case, 20). It is calculated in the follow way:

If we only consider the first row (since other rows are the same), then out of the 67 elements in the first row, the first 7 would go to the first cell of the output layer. Then the filter moves 3 element to the right, which makes the filter covering element 4 to 10, and that would correspond to the 2nd element of the output layer. So on so forth. Every time we advance 3 elements and the total number of times we will advance (counting the first step where it covers 7 elements) is n. Thus the equation I used.

However, the output layer I got from Tensorflow was 23, which is 67/3 and rounded up to the next integer. I don't understand the reasoning behind this.

Can someone explain why it is done like this in Tensorflow?

Thanks!

Answer 1

Output size is computed in two ways depending on the padding you are using. If you are using 'SAME' padding, the output size is computed as:

out_height = ceil(float(in_height) / float(strides[1]))
out_width  = ceil(float(in_width) / float(strides[2]))

Where as with 'VALID' padding output is computed as:

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

Which is what you were using to calculate your output, but we can clearly see you must be using 'SAME' padding.

So in your case you get:

If you were actually using 'VALID' padding, the output would be closer to your approximation.

You can read more about how tensorflow calculates feature map sizes and padding here.