please help making a loop faster

[ThomasL]

This was my first solution. This will already give you a speed boost.

def forward_path_half_vectorized(x, w, b):
    """
    Inputs:
    - x: A numpy array of images of shape (N, H, W)
    - w: A numpy array of weights of shape (M, H, W)
    - b: A numpy vector of biases of size M
 
    Outputs: 
    - cout: a numpy array of shape (N, M)
    """

    N, _, _ = x.shape
    M, _, _ = w.shape
    cout = np.zeros((N, M))

    for ni in range(N):
        for mi in range(M):
            cout[ni, mi] = np.sum(x[ni] * w[mi])
    
    return cout + b

But I thought there must be a better way and i found it looking through the numpy documentation.
https://docs.scipy.org/doc/numpy/referen...ordot.html

def forward_path_full_vectorized(x, w, b):
    """
    Inputs:
    - x: A numpy array of images of shape (N, H, W)
    - w: A numpy array of weights of shape (M, H, W)
    - b: A numpy vector of biases of size M
 
    Outputs: 
    - cout: a numpy array of shape (N, M)
    """
    
    return np.tensordot(x, w, axes=([1,2],[1,2])) + b

The full vectorized version is even 90 times faster !

X = np.ones((100, 64, 64), dtype=np.float64) * 0.3
W = np.ones((200, 64, 64), dtype=np.float64) * 1.5
B = np.ones((200), dtype=np.float64) * 3.3

%timeit forward_path_half_vectorized(X, W, B)
-> 408 ms ± 2.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit forward_path_full_vectorized(X, W, B)
-> 4.63 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Life can be easy knowing where to look. :-)