W3_A1_Ex-8_Assertion Error for Optimize function

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

Hello,

I am having this error

AssertionError                            Traceback (most recent call last)
<ipython-input-85-9408a3dffbf6> in <module>
      1 from public_tests import *
      2 
----> 3 model_test(model)

~/work/release/W2A2/public_tests.py in model_test(target)
    131     assert type(d['w']) == np.ndarray, f"Wrong type for d['w']. {type(d['w'])} != np.ndarray"
    132     assert d['w'].shape == (X.shape[0], 1), f"Wrong shape for d['w']. {d['w'].shape} != {(X.shape[0], 1)}"
--> 133     assert np.allclose(d['w'], expected_output['w']), f"Wrong values for d['w']. {d['w']} != {expected_output['w']}"
    134 
    135     assert np.allclose(d['b'], expected_output['b']), f"Wrong values for d['b']. {d['b']} != {expected_output['b']}"

AssertionError: Wrong values for d['w']. [[ 0.14449502]
 [-0.1429235 ]
 [-0.19867517]
 [ 0.21265053]] != [[ 0.08639757]
 [-0.08231268]
 [-0.11798927]
 [ 0.12866053]]

Does anyone can help me ?

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2

That type of error typically means that you have passed incorrect arguments to optimize when you call that from model. You need to pass all the parameters, but not hard-code any of them. If there are any equal signs in the parameter list, that means you are ignoring the values being passed into model at the top level. If you don’t include the learning rate or number of iterations, that also means you are ignoring the values and using the defaults defined in the optimize function.

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