Difference between np.dot and np.multiply with np.sum in binary cross-entropy loss calculation

Question

I have tried the following code but didn\'t find the difference between np.dot and np.multiply with np.sum

Here is np.dot

Answer 1

If Y and A2 are (1,N) arrays, then np.dot(Y,A.T) will produce a (1,1) result. It is doing a matrix multiplication of a (1,N) with a (N,1). The N's are summed, leaving the (1,1).

With multiply the result is (1,N). Sum all values, and the result is a scalar.

If Y and A2 were (N,) shaped (same number of elements, but 1d), the np.dot(Y,A2) (no .T) would also produce a scalar. From np.dot documentation:

For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors

Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned.

squeeze reduces all size 1 dimensions, but still returns an array. In numpy an array can have any number of dimensions (from 0 to 32). So a 0d array is possible. Compare the shape of np.array(3), np.array([3]) and np.array([[3]]).