Week 2 Programming Assignment Logistic Regression with a Neural Network Mindset

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning
3

@kenb There is only one implementation of the propogate() which is reused by the optimize()

The propagate_test() passes with the following output:

dw = [[0.99845601]
[2.39507239]]
db = 0.001455578136784208
cost = 5.801545319394553
All tests passed!

I implemented a second way of calculating the cost using np.sum() which does result in a scalar output but the result from the optimize_test() is the same

5.801545319394553
w = [[0.17998823]
[0.07550727]]
b = 0.8098329520228539
dw = [[0.61299061]
[1.31774648]]
db = 0.0633680265420237
Costs = [array(5.80154532)]
5.801545319394553
0.5892317480074818

AssertionError Traceback (most recent call last)
in
7 print("Costs = " + str(costs))
8
----> 9 optimize_test(optimize)

~/work/release/W2A2/public_tests.py in optimize_test(target)
61 assert type(costs) == list, “Wrong type for costs. It must be a list”
62 assert len(costs) == 2, f"Wrong length for costs. {len(costs)} != 2"
—> 63 assert np.allclose(costs, expected_cost), f"Wrong values for costs. {costs} != {expected_cost}"
64
65 assert type(grads[‘dw’]) == np.ndarray, f"Wrong type for grads[‘dw’]. {type(grads[‘dw’])} != np.ndarray"

AssertionError: Wrong values for costs. [array(5.80154532), array(0.58923175)] != [5.80154532, 0.31057104]