Hello, I am having a problem with the Logistic Regression final exercise. When implementing the model function I get the following error although my code has passed all previous tests.
AssertionError: Wrong values for d[‘w’]. [[ 0.14449502]
[-0.1429235 ]
[-0.19867517]
[ 0.21265053]] != [[ 0.08639757]
[-0.08231268]
[-0.11798927]
[ 0.12866053]]
A part of the code for the model function is the following:
{moderator edit - solution code removed}
You are calling optimize incorrectly. Notice that you do not pass the number of iterations and learning rate. So what will that mean in terms of what values actually get used? I added print statements at the beginning of model and at the beginning of optimize and here’s what I see when I run that test case:
model with num_iterations 50 learning_rate 0.01
before optimize w.shape (4, 1)
optimize with num_iterations 50 learning_rate 0.01
in model before predict call w.shape (4, 1)
predictions = [[1. 1. 0. 1. 0. 0. 1.]]
predictions = [[1. 1. 0.]]
costs = [array(0.69314718)]
All tests passed!
Please try the same thing and you will see what is happening. Note that this course assumes you are already an experienced python programmer. If you are not familiar with how optional or “keyword” parameters work in python, it would be a good idea to google “python keyword parameters” and read a tutorial about how they work.
Thank you so much i found the issue and now all tests pass!