I am using tensorflow to construct a convolution neural network. Given a tensor of the shape (none, 16, 16, 4, 192) I want to perform a transpose convolution that results in the shape (none, 32, 32, 7, 192).
Would a filter size of [2,2,4,192,192] and stride of [2,2,1,1,1] produce the output shape that I want?
Yes, you are almost right.
One minor correction is that tf.nn.conv3d_transpose expects NCDHW or NDHWC input format (yours appears to be NHWDC) and the filter shape is expected to be [depth, height, width, output_channels, in_channels]. This affects the order of dimensions in the filter and stride:
# Original format: NHWDC.
original = tf.placeholder(dtype=tf.float32, shape=[None, 16, 16, 4, 192])
print original.shape
# Convert to NDHWC format.
input = tf.reshape(original, shape=[-1, 4, 16, 16, 192])
print input.shape
# input shape: [batch, depth, height, width, in_channels].
# filter shape: [depth, height, width, output_channels, in_channels].
# output shape: [batch, depth, height, width, output_channels].
filter = tf.get_variable('filter', shape=[4, 2, 2, 192, 192], dtype=tf.float32)
conv = tf.nn.conv3d_transpose(input,
filter=filter,
output_shape=[-1, 7, 32, 32, 192],
strides=[1, 1, 2, 2, 1],
padding='SAME')
print conv.shape
final = tf.reshape(conv, shape=[-1, 32, 32, 7, 192])
print final.shape
Which outputs:
(?, 16, 16, 4, 192)
(?, 4, 16, 16, 192)
(?, 7, 32, 32, 192)
(?, 32, 32, 7, 192)