The number that doesn’t match there is the number of trainable parameters in the layer. Note that your value is 512 less than twice the expected value. That suggests that what is wrong has to do with either the filter size or number of channels. But in either case, you’d expect more problems in the next layer as well.
Actually, I think this is just a problem in the order in which the rows are added to the model description. Take a look at the “expected” value in the outputs.py file. Note that there is one case in which there are two adjacent layers that look just like your layer and the one it expects. I’ll bet they ended up in the opposite order in your code. One theory would be that it builds the graph in the order that things are referenced. I’ll bet you did the following “Add” layer with the arguments in the opposite order. Addition is commutative, of course, so you’d expect it not to matter, but maybe in this case it does. 
Update: Yes! I can produce exactly that error by reversing the order of the operands on the Add for the shortcut layer in the convolutional_block. Try reversing those operands and I’ll bet the problem goes away!