Compute and display gradient with w initialized to zeroes
initial_w = 0
initial_b = 0
tmp_dj_dw, tmp_dj_db = compute_gradient(x_train, y_train, initial_w, initial_b)
print(‘Gradient at initial w, b (zeros):’, tmp_dj_dw, tmp_dj_db)
compute_gradient_test(compute_gradient)
Gradient at initial w, b (zeros): -566.3960999999998 -0.060197268572643195
Using X with shape (4, 1)
AssertionError Traceback (most recent call last)
in
6 print(‘Gradient at initial w, b (zeros):’, tmp_dj_dw, tmp_dj_db)
7
----> 8 compute_gradient_test(compute_gradient)
~/work/public_tests.py in compute_gradient_test(target)
51 #assert dj_dw.shape == initial_w.shape, f"Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}"
52 assert dj_db == 0.0, f"Case 1: dj_db is wrong: {dj_db} != 0.0"
—> 53 assert np.allclose(dj_dw, 0), f"Case 1: dj_dw is wrong: {dj_dw} != [[0.0]]"
54
55 # Case 2
AssertionError: Case 1: dj_dw is wrong: 2.0 != [[0.0]]
How to rectify the code without the assertion error?