Hello @NeelD1999,
Welcome and good job working on the logistic regression problem. You are almost there!
If you check the comments for this particular task, you get the advice to use np.dot for the cost computation (# compute cost using np.dot. Don't use loops for the sum.). However, you can also use np.sum, if you make sure the shape returned corresponds to the shape returned by using np.dot for this task.
I will give you an example that hopefully helps you understand the difference between np.sum and np.dot for this task:
import numpy as np
v = np.array([[1.],[2.],[3.]])
print(v)
cost = np.sum(v)
print("cost:", cost)
print("cost.dtype:", cost.dtype)
print("cost.shape:", cost.shape)
cost2 = np.sum(v, keepdims=True)
print("cost2:", cost2)
print("cost2.dtype:", cost2.dtype)
print("cost2.shape:", cost2.shape)
ones = np.array([[1],[1],[1]])
cost3 = np.dot(ones.T, v)
print("cost3:", cost3)
print("cost3.dtype:", cost3.dtype)
print("cost3.shape:", cost3.shape)
Output:
[[1.]
[2.]
[3.]]
cost: 6.0
cost.dtype: float64
cost.shape: ()
cost2: [[6.]]
cost2.dtype: float64
cost2.shape: (1, 1)
cost3: [[6.]]
cost3.dtype: float64
cost3.shape: (1, 1)
You can read more about np.sum on numpy.sum — NumPy v1.20 Manual
Try changing the cost calculation to only using np.dot operations. It is a good exercise in linear algebra.
Please let me know if you have any additional questions.