Neural network weights are better viewed as source code because they specify what function the network computes. As we're operating purely on feed-forward networks, there are no loops. Therefore, weights fully describe everything relevant for executing their represented function on inputs. Weights can be seen as a sort of intermediate language (with lots of stored data and partially computed states) interpretable by some deep learning library.

The network architecture itself is not source code, but a rough specification constraining the optimizer, which searches for possible program descriptions that within the specified constraints, minimize some loss function with respect to the data.

Neither data nor network architecture are the actual source, they are better seen as recipes which if followed (will at great expense), allow finding behaviorally similar programs. As you can see, the standard ideas of open source don’t quite carry over because the actual "source-code" is not human interpretable.