# Week 3 Exercise 8 - nn_model AssertionError: Wrong values for W1

{moderator edit - solution code removed}

## Cost after iteration 0: 0.692739 Cost after iteration 1000: 0.000218 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: 0.000023 W1 = [[-0.65848169 1.21866811] [-0.76204273 1.39377573] [ 0.5792005 -1.10397703] [ 0.76773391 -1.41477129]] b1 = [[ 0.287592 ] [ 0.3511264 ] [-0.2431246 ] [-0.35772805]] W2 = [[-2.45566237 -3.27042274 2.00784958 3.36773273]] b2 = [[0.20459656]]

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

You are not passing the learning rate to `update_parameters`. That means you get the default value declared in the definition of the function. Is that the same as the value being actually passed?