I am trying to understand how to calculate the number of parameters for the transposed convolution. For example, let’s take the U-net in the second assignment of Week 3 of Course 4.

According to the dump of the model, the first transposed convolution takes as input a volume with dimensions (6, 8, 512) and outputs a volume with dimensions (12, 16, 256). In our case, the filter is 2x2. So, I would say that each filter is really 2x2x512 and there are 256 of such filters, so that we get about half a million parameters. The dump of the model says that there are more than a million parameters for this layer. Why is this the case?