

In [5]:

    
"""
Set the values to `strides` and `ksize` such that
the output shape after pooling is (1, 2, 2, 1).
"""
import tensorflow as tf
import numpy as np
import math



In [6]:

    
# `tf.nn.max_pool` requires the input be 4D (batch_size, height, width, depth)
# (1, 4, 4, 1)
x = np.array([
    [0, 1, 0.5, 10],
    [2, 2.5, 1, -8],
    [4, 0, 5, 6],
    [15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
X = tf.constant(x)

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]))



In [7]:

    
print("out_height = ", math.ceil(float(4 - 2 + 1) / float(2)))
print("out_width  = ", math.ceil(float(4 - 2 + 1) / float(2)))









    



out_height =  2
out_width  =  2



In [8]:

    
def maxpool(input):
    # TODO: Set the ksize (filter size) for each dimension (batch_size, height, width, depth)
    ksize = [1, 2, 2, 1]
    # TODO: Set the stride for each dimension (batch_size, height, width, depth)
    strides = [1, 2, 2, 1]
    # TODO: set the padding, either 'VALID' or 'SAME'.
    padding = 'VALID'
    # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#max_pool
    return tf.nn.max_pool(input, ksize, strides, padding)



In [9]:

    
out = maxpool(X)



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