

In [17]:

    
"""
Setup the strides, padding and filter weight/bias such that
the output shape is (1, 2, 2, 3).
"""
import tensorflow as tf
import numpy as np
import math



In [18]:

    
# `tf.nn.conv2d` 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))
print(x)
X = tf.constant(x)









    



[[[[  0. ]
   [  1. ]
   [  0.5]
   [ 10. ]]

  [[  2. ]
   [  2.5]
   [  1. ]
   [ -8. ]]

  [[  4. ]
   [  0. ]
   [  5. ]
   [  6. ]]

  [[ 15. ]
   [  1. ]
   [  2. ]
   [  3. ]]]]

I want to transform the input shape (1, 4, 4, 1) to (1, 2, 2, 3). 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 [21]:

    
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 [25]:

    
def conv2d(input):
    # Filter (weights and bias)
    # The shape of the filter weight is (height, width, input_depth, output_depth)
    # The shape of the filter bias is (output_depth,)
    # TODO: Define the filter weights `F_W` and filter bias `F_b`.
    # NOTE: Remember to wrap them in `tf.Variable`, they are trainable parameters after all.
    #print(input)
    F_W = tf.Variable(tf.truncated_normal((2,2,1,3)))
    F_b = tf.Variable(tf.Variable(tf.zeros(3)))
    
    # 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#conv2d
    # `tf.nn.conv2d` does not include the bias computation so we have to add it ourselves after.
    return tf.nn.conv2d(input, F_W, strides, padding) + F_b



In [26]:

    
out = conv2d(X)



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