Hello, I get the following error on exercise 8 of the Logistic Regression with a Neural Network mindset task. My understanding is that when model_test runs this test, it is using some hypothetical data which I do not have access to. My code works, but I end up with training accuracy of 68% and test accuracy of 34%. I then find that when I plot the cost function over iterations, nothing gets plotted. Do you have any idea what could be going wrong?

AssertionError Traceback (most recent call last)

in

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]]