35. Solution: TensorFlow Pooling Layer
Solution
Here's how I did it. NOTE : there's more than 1 way to get the correct output shape. Your answer might differ from mine.
def maxpool(input):
ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = 'VALID'
return tf.nn.max_pool(input, ksize, strides, padding)
I want to transform the input shape
(1, 4, 4, 1)
to
(1, 2, 2, 1)
. I choose
'VALID'
for the padding algorithm. I find it simpler to understand and it achieves the result I'm looking for.
out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))
out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))
Plugging in the values:
out_height = ceil(float(4 - 2 + 1) / float(2)) = ceil(1.5) = 2
out_width = ceil(float(4 - 2 + 1) / float(2)) = ceil(1.5) = 2
The depth doesn't change during a pooling operation so I don't have to worry about that.