C1_W3 Wrong weight value error

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

Hi, I am facing a error in The Planar Data Classification assignment of week 3 of Neural networks and Deep Learning. So, in the nn_model function I am calling all the functions correctly and with right parameters and the test cases of above functions have passed successfully, still whenever I am running nn_model_test(nn_model)
Wrong Weight value error.
Failing output:
Cost after iteration 0: 0.693395
Cost after iteration 1000: 0.000217
Cost after iteration 2000: 0.000107
Cost after iteration 3000: 0.000071
Cost after iteration 4000: 0.000053
Cost after iteration 5000: 0.000042
Cost after iteration 6000: 0.000035
Cost after iteration 7000: 0.000030
Cost after iteration 8000: 0.000026
Cost after iteration 9000: 0.000024
W1 = [[-0.73679287 -1.36621711]
[-0.62039407 -1.13347599]
[-0.71454509 -1.31939197]
[ 0.74580119 1.37837548]]
b1 = [[ 0.01230887]
[-0.00176212]
[ 0.00896606]
[-0.01164917]]
W2 = [[-3.04892822 -2.13993848 -2.84526122 3.11015916]]
b2 = [[0.0041659]]

AssertionError Traceback (most recent call last)
in
----> 1 nn_model_test(nn_model)

~/work/release/W3A1/public_tests.py in nn_model_test(target)
292 assert output[“b2”].shape == expected_output[“b2”].shape, f"Wrong shape for b2."
293
→ 294 assert np.allclose(output[“W1”], expected_output[“W1”]), “Wrong values for W1”
295 assert np.allclose(output[“b1”], expected_output[“b1”]), “Wrong values for b1”
296 assert np.allclose(output[“W2”], expected_output[“W2”]), “Wrong values for W2”

AssertionError: Wrong values for W1

2

Sorry, but that evidence suggests that there is something wrong with your nn_model code. If it’s all correct, then why does the test fail? Just because the previous functions pass their tests is not enough, right? You can still call them incorrectly or pass them incorrect arguments.

To get to the next step in the debugging process, please “copy/paste” the full output you get from the failing test.

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