W3A1: Problems that I encountered due to public_tests.backward_propagation_test

[paulinpaloalto]

@Shriesh_Kumar

2. But the cache values (A and Z for both layers) were also being generated randomly. Shouldn’t these values be calculated from the already initialized W’s and b’s, and then passed into target /backward_propagation ? My reasoning being the gradient has to be taken out at a particular point defined by the current state of values in parameters and the input X, which also determine the values of cache at that instant. So this random generation of the cache values caught my eye.

Thanks for your careful analysis of what is happening here. Yes, you’re right that the test case here is a bit sketchy. This issue has been known for a long time: here’s a thread about it from November 2021. I filed a bug about this in March 2023, but the course staff has decided that it is low priority enough that they have not done anything about it yet. Note that they literally wrote out the correct code for g^{[1]'}(Z^{[1]}) for you in the instructions. See the “Tips” section right before the function:

If you instead implement it as:

(1 - np.tanh(Z1)**2)

then it passes the first visible test in the notebook, but it fails the one you pointed out that is imported because in that test case it is not true that:

A1 = np.tanh(Z1)

because they just randomly generated A1 and Z1.