I am attempting to stride over the channel dimension, and the following code exhibits surprising behaviour. It is my expectation that tf.nn.max_pool and tf.nn.avg_pool should produce tensors of identical shape when fed the exact same arguments. This is not the case.
import tensorflow as tf
x = tf.get_variable('x', shape=(100, 32, 32, 64),
initializer=tf.constant_initializer(5), dtype=tf.float32)
ksize = (1, 2, 2, 2)
strides = (1, 2, 2, 2)
max_pool = tf.nn.max_pool(x, ksize, strides, padding='SAME')
avg_pool = tf.nn.avg_pool(x, ksize, strides, padding='SAME')
print(max_pool.shape)
print(avg_pool.shape)
This prints
$ python ex04/mini.py
(100, 16, 16, 32)
(100, 16, 16, 64)
Clearly, I am misunderstanding something.
