Got error in the code - python changed its random generator?

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning
1

{moderator edit - solution code removed}

np.random.seed(2)
n_x, n_h, n_y = initialize_parameters_test_case()
parameters = initialize_parameters(n_x, n_h, n_y)

print("W1 = " + str(parameters[“W1”]))
print("b1 = " + str(parameters[“b1”]))
print("W2 = " + str(parameters[“W2”]))
print("b2 = " + str(parameters[“b2”]))

initialize_parameters_test(initialize_parameters)

--------------got this error---------------------
W1 = [[0.00435995 0.00025926]
[0.00549662 0.00435322]
[0.00420368 0.00330335]
[0.00204649 0.00619271]]
b1 = [[0.]
[0.]
[0.]
[0.]]
W2 = [[0.00299655 0.00266827 0.00621134 0.00529142]]
b2 = [[0.]]

AssertionError Traceback (most recent call last)
in
8 print("b2 = " + str(parameters[“b2”]))
9
—> 10 initialize_parameters_test(initialize_parameters)

~/work/release/W3A1/public_tests.py in initialize_parameters_test(target)
57 assert parameters[“b2”].shape == expected_output[“b2”].shape, f"Wrong shape for b2."
58
—> 59 assert np.allclose(parameters[“W1”], expected_output[“W1”]), “Wrong values for W1”
60 assert np.allclose(parameters[“b1”], expected_output[“b1”]), “Wrong values for b1”
61 assert np.allclose(parameters[“W2”], expected_output[“W2”]), “Wrong values for W2”

AssertionError: Wrong values for W1

2

No, the problem is you used the wrong random function. The clue is that all your output values are positive. That would not be true if you used a normal distribution with \mu = 0 and \sigma = 1. Please have a more careful look at the instructions. They basically wrote the code out for you in the instructions.

3

Thanks a lot for your quick reply. I did not notice we need to use normal. That helps

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