Hello. I passed all the tests before Exercise 8, but for it I got an error–>
ValueError: shapes (1,3) and (4,3) not aligned: 3 (dim 1) != 4 (dim 0)
I know that is size of X and w.T
But I went through my codes and did not find any hardcoding. Also I looked the others comments and the same, could not find the answer to my problem.
Please, help me to find the problem.
Thank you in advance.
Hi and welcome @Lilit.Ghalachyan,
Which line of code gives you this exception?
ValueError Traceback (most recent call last)
in
----> 1 model_test(model)
~/work/release/W2A2/public_tests.py in model_test(target)
109 y_test = np.array([1, 0, 1])
110
→ 111 d = target(X, Y, x_test, y_test, num_iterations=50, learning_rate=1e-4)
112
113 assert type(d[‘costs’]) == list, f"Wrong type for d[‘costs’]. {type(d[‘costs’])} != list"
in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost)
38 w,b=initialize_with_zeros(X_train.reshape(X_train.shape[0], -Y_train.shape[1]).T.shape[0])
39
—> 40 parameters, grads, costs =optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
41
42 w=params[“w”]
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)
29 # cost = …
30 # YOUR CODE STARTS HERE
—> 31 A=sigmoid(np.dot(w.T,X)+b)
32 cost=-1/m*np.sum((np.dot(np.log(A),Y.T),(np.dot(np.log(1-A),(1-Y).T))))
33 # YOUR CODE ENDS HERE
<array_function internals> in dot(*args, **kwargs)
ValueError: shapes (1,3) and (4,3) not aligned: 3 (dim 1) != 4 (dim 0)
Hint:
doesn’t look right 
I changed it to dimension of w,
w,b=initialize_with_zeros(X_train.shape[1])
ValueError Traceback (most recent call last)
in
----> 1 model_test(model)
~/work/release/W2A2/public_tests.py in model_test(target)
109 y_test = np.array([1, 0, 1])
110
→ 111 d = target(X, Y, x_test, y_test, num_iterations=50, learning_rate=1e-4)
112
113 assert type(d[‘costs’]) == list, f"Wrong type for d[‘costs’]. {type(d[‘costs’])} != list"
in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost)
38 w,b=initialize_with_zeros(X_train.shape[1])
39
—> 40 parameters, grads, costs =optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
41
42 w=params[“w”]
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)
29 # cost = …
30 # YOUR CODE STARTS HERE
—> 31 A=sigmoid(np.dot(w.T,X)+b)
32 cost=-1/m*np.sum((np.dot(np.log(A),Y.T),(np.dot(np.log(1-A),(1-Y).T))))
33 # YOUR CODE ENDS HERE
<array_function internals> in dot(*args, **kwargs)
ValueError: shapes (1,3) and (4,3) not aligned: 3 (dim 1) != 4 (dim 0)
Think about the dimensions of w, b, and X.
Inside propagate, you can always print(w.shape) and print(X.shape) to see if your matrix multiplication works or not.
In the general case, A x B, the number of columns of A needs to match the number of rows of B.
You still have an error in your initialization call.
In my propagate function I wrote A=sigmoid(np.dot(w.T,X)+b), so the matrix multiplication works.
In initialization call I should write number of parameters which is X_train.shape[1], isn’t it?
I am sorry, I looked through the codes and still can’t find the error((
The matrix multiplication only works if w has been initialized correctly. I advise you to print(X_train.shape). What is X_train.shape[0]? What is X_train.shape[1]? You can also look at the videos again, where they talk about vectorization and the layout of X. Hopefully, that will give you a hint for how to initialize your params using the dimensions of X_train. As I also said in my earlier post, print the shapes inside propagate and you will see that the matrix multiplication does not work 
ok thank you very much.
You are welcome! You can never debug and print too much to figure out what is going on when you encounter a problem.