~/work/release/W2A2/public_tests.py in initialize_with_zeros_test_1(target)
19 assert b == 0., “b must be 0.0”
20 assert type(w) == np.ndarray, f"Wrong type for w. {type(w)} != np.ndarray"
—> 21 assert w.shape == (dim, 1), f"Wrong shape for w. {w.shape} != {(dim, 1)}"
22 assert np.allclose(w, [[0.], [0.], [0.]]), f"Wrong values for w. {w} != {[[0.], [0.], [0.]]}"
23 print(‘\033[92mFirst test passed!’)
The tests are sending a few AssertionErrors here. These errors are thrown by the unit tests used to help you debug your code. Read through them carefully; they are your friends.
Line 19 reports that you have probably specified model parameter b as a Python type int rather than as type float. For example, I could write x = 7 or I could write x = 7.0. A language such as Matlab doesn’t care–they would both be interpreted as floating point numbers.
Python, however, makes the distinction. When it comes to mathematical analysis (any numbers that we want to apply mathematical operators to), we are almost exclusively interested in floating points. In which case, we want to use the latter statement: x = 7.0. It is also acceptable (and more concise) to write x = 7. It's the popular choice too. Also, any int can be converted to a float with the built-in function float() as in: x = float(7), which of course, is way less concise!
And then you have the wrong shape for w. You have produced an array of dimension-0, i.e, giving dimensions (3,), where an array of dimesion-1 is required, i.e. dimension (3, 1). The instructions to the exercise ask you to look up np.zeros() in the Numpy library documentation. Please do!
