Stuck on compute_gradient function

Hello Gerardo,

Your code for C2 is incorrect/incomplete. I am sharing screenshot of the hints which is mentioned just below the grader cell to make your code correct.


GRADED FUNCTION: compute_cost

def compute_cost(X, y, w, b, *argv):

If you are still running into error, let me know.

Remember the loss_sum is the label in a labeled example. Since this is logistic regression, every value of must either be 0 or 1. In your case you have not initialise loss_sum=0. See the hint images, you will understand.

Also for C3 where you have mentioned f_wb = sigmoid(z_wb) is incorrect as you have initialise z_wb to 0 in the initial line of code

Did you read this before C3 cell

So here for
f_wb apply sigmoid function to the both parameters with the number of examples and while applying sigmoid function remember this hint
As you are doing this, remember that the variables X_train and y_train are not scalar values but matrices of shape ( 𝑚,𝑛 ) and ( 𝑚 ,1) respectively, where 𝑛 is the number of features and 𝑚 is the number of training examples.

  1. This code is incorrect. Use hint section to make this code one line.
    for j in range(n):
    dj_dw_ij = (f_wb - y[i])* X[i][j]
    dj_dw += dj_dw_ij

  2. At the end divide dj_db and dj_dw by total number of examples
    dj_dw = dj_dw / m
    dj_db = dj_db / m