TensorFlow Pooling Layer

Using Pooling Layers in TensorFlow

In the below exercise, you'll be asked to set up the dimensions of the pooling filters, strides, as well as the appropriate padding. You should go over the TensorFlow documentation for tf.nn.max_pool(). Padding works the same as it does for a convolution.

Instructions

Finish off each TODO in the maxpool function.
Setup the strides, padding and ksize such that the output shape after pooling is (1, 2, 2, 1).

Start Quiz:

pool.py

"""
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

# `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)

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

User's Answer:

(Note: The answer done by the user is not guaranteed to be correct)

pool.py

"""
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

# `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)

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 = 'SAME'
    # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#max_pool
    return tf.nn.max_pool(input, ksize, strides, padding)
    
out = maxpool(X)