How to use the transpose convolution function in TensorFlow

conv_xpose.py

## a batch of 3 4x4x2 images as input.  We will upsample these to 8x8
input_data = np.ones([3, 4, 4, 2])

filter_shape = [3,3,4,2]  # width, height, channels-out, channels-in
tconv_filter = np.ones(filter_shape)  # make the filter all ones so that we can manually calculate the output
                                      # obviously, having all the filter channels have the same coefficients defeats
                                      # the purpose of having multiple channels, but this is just an example
output_shape = [8, 8, 4] # width, height, channels-out  (notice we don't have the batch size dimension -- more on that later)

## set up the slots for the data
xin = tf.placeholder(dtype=tf.float32, shape = (None, 4, 4, 2), name='input')
filt = tf.placeholder(dtype=tf.float32, shape = filter_shape, name='filter')

## Run the transpose convolution.  With a stride of 2, this should upsample our image by a factor of 2
##   You have to specify the output shape, but as far as I can tell, it's not a free parameter; it's determined
##   by the choice of stride.  What's annoying is that the first dimension is usually unknown, but we have to 
##   include it, so we have to extract it and concatenate it onto the front of output_shape.
dimxin = tf.shape(xin)
ncase = dimxin[0:1]
oshp = tf.concat([ncase,output_shape], axis=0)
z1 = tf.nn.conv2d_transpose(xin, filt, oshp, strides=[1,2,2,1], name='xpose_conv')

## tf.layers has an all-in-one transpose convolution layer.  It's a lot more convenient, but
##   you don't get to specify the weights (they get initialized randomly). In this case I wanted to use
##   specified weights so I could see what the actual effect is.  Note that while the default padding for
##   tf.nn.conv2d_transpose is 'same', the default for this function is 'valid'.  You're @#$%ing killing
##   me, Google.
z2 = tf.layers.conv2d_transpose(xin, 4, (3,3), strides=(2,2), padding='SAME')

with tf.Session() as sess:
    summary_writer = tf.summary.FileWriter('logs', sess.graph)

    sess.run(tf.global_variables_initializer())

    (z1out, z2out) = sess.run(fetches=[z1,z2],
                    feed_dict={xin:input_data, filt:tconv_filter})

    print(z1out.shape)
    print(z2out.shape)
    print(z1out[0, ..., 0])
    

Here's the output:

(3, 8, 8, 4)
(3, 8, 8, 4)
array([[2., 2., 4., 2., 4., 2., 4., 2.],
       [2., 2., 4., 2., 4., 2., 4., 2.],
       [4., 4., 8., 4., 8., 4., 8., 4.],
       [2., 2., 4., 2., 4., 2., 4., 2.],
       [4., 4., 8., 4., 8., 4., 8., 4.],
       [2., 2., 4., 2., 4., 2., 4., 2.],
       [4., 4., 8., 4., 8., 4., 8., 4.],
       [2., 2., 4., 2., 4., 2., 4., 2.]], dtype=float32)

z2out will be filled with random values, since the weights are initialized randomly.

Incidentally, in the output above you can see the well-known checkerboard pattern that transpose convolutions are often criticized for. Many sources recommend following this operation with a same-size convolution to smooth out the artifacts.