W2 A2 | Ex-4 | Code seems correct by all accounts but not passing the tests

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

On week two programming assignments, I have the code following the logic and everything should be working right, but I am not just passing the tests. Here is more to what I am saying:

  1. for the initialization problem, where we have to initialize the parameters w, and b, I have the correct logic or so I think, but my code is not passing the tests. There is an assertion error statement but it doesn’t say any thing, here is the error text:

AssertionError Traceback (most recent call last)
in
2 w, b = initialize_with_zeros(dim)
3
----> 4 assert type(b) == float
5 print ("w = " + str(w))
6 print ("b = " + str(b))

AssertionError:

What could be the issue, can I get the TA to help or something?

  1. The same happens with the next question for forward and backward propagation problem, here is the error code again:

AssertionError Traceback (most recent call last)
in
5 grads, cost = propagate(w, b, X, Y)
6
----> 7 assert type(grads[“dw”]) == np.ndarray
8 assert grads[“dw”].shape == (2, 1)
9 assert type(grads[“db”]) == np.float64

AssertionError:

  1. For the next question I have the following error code even though I have not done anything to change to affect the dimensions of the train and test tests,

ValueError Traceback (most recent call last)
in
----> 1 params, grads, costs = optimize(w, b, X, Y, num_iterations=100, learning_rate=0.009, print_cost=False)
2
3 print ("w = " + str(params[“w”]))
4 print ("b = " + str(params[“b”]))
5 print ("dw = " + str(grads[“dw”]))

in optimize(w, b, X, Y, num_iterations, learning_rate, print_cost)
35 # grads, cost = …
36 # YOUR CODE STARTS HERE
—> 37 grads, cost = propagate(w, b, X, Y)
38
39 # YOUR CODE ENDS HERE

in propagate(w, b, X, Y)
30 # cost = …
31 # YOUR CODE STARTS HERE
—> 32 A = sigmoid(np.dot(w.T, X) + b)
33 cost = -1./m* np.sum(Y*np.log(A) + (1-Y)*np.log(1-A))
34

ValueError: operands could not be broadcast together with shapes (1,3) (2,2)

and so is the last question.

Any help will be really helpful! Thanks

2

The first order of business is to free yourself from the idea that your code is correct. If the tests fail, that means your code is not correct, so now you need to dig deeper and understand what the failures are telling you.

It sounds like this is your first encounter with the idea of “assertions”. An assertion is a statement that checks for a condition that is supposed to be true. If the condition turns out not to be true, then it throws an exception. Let’s look at the first one you show above:

So the assertion there says that the type of your b value needs to be float. The fact that assertion fails means the type was not a floating point type. Note that in python 0 and 0. are not the same thing. The former is an integer and the latter is a floating point value.

Now that you’ve had an introduction to how to interpret the failure of an assertion, apply that to the next one:

So that says that the dw value produced by your code is not a numpy array. So what type is it? Add a print statement like this to your code:

print(type(dw))

What does that show? Why is it not a numpy array?

For the last failure, my guess is that your b value is causing that failure. It should be a scalar, but it apparently turns out to be a 2 x 2 array. So how did that happen? It could be that your db value is wrong and/or that your “update parameters” logic is wrong.

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