Help! Optimization methods

Course Q&A Deep Learning Specialization Improving Deep Neural Networks: Hyperparameter tun
1

Please see the Error:

W1 =
[[ 1.63942888 -0.61624001 -0.54303153]
[-1.05781906 0.85024927 -2.31663159]]
W2 =
[[ 0.33400701 -0.23919264]
[ 1.47721313 -2.04655625]
[-0.33719642 -0.36907981]]
b1 =
[[ 1.73008524]
[-0.77629952]]
b2 =
[[ 1.14819595]
[-1.08492045]
[-0.15735887]]

AssertionError Traceback (most recent call last)
in
7 print(f"b2 = \n{parameters[‘b2’]}")
8
----> 9 update_parameters_with_adam_test(update_parameters_with_adam)

~/work/release/W2A1/public_tests.py in update_parameters_with_adam_test(target)
280 assert type(parameters[key]) == np.ndarray, f"Wrong type for parameters[‘{key}’]. Expected np.ndarray"
281 assert parameters[key].shape == parametersi[key].shape, f"Wrong shape for parameters[‘{key}’]. The update must keep the dimensions of parameters inputs"
→ 282 assert np.allclose(parameters[key][0], expected_parameters[key]), f"Wrong values. Check you formulas for parameters[‘{key}’]"
283 #print(f"{key}: \n {str(parameters[key])}")
284

AssertionError: Wrong values. Check you formulas for parameters[‘W1’]

2

Check you update parameters for the grader cell

make sure you recalled with the right function for parameter W1.

3

Hallo

It’s week 2, Optimization methods.

4

One more thing, epsilon is not under the square root.

5

I finally, I found the solution.

The final parameter formula is missing a bracket, which makes me almost blind. . .