please help making a loop faster

[carla_highlander]

I'm working on a forward pass for a neural network. I have written loop within loop within loops. I know there's a way to do this in numpy that is much faster and simpler.

def forward_p(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, H, W = x.shape
    M, _, _ = w.shape
    cout = np.zeros((N,M))

    for ni in range(N):
        for mi in range(M):
                cout[ni,mi] = b[mi]
                for d1 in range(H):
                    for d2 in range(W):
                        cout[ni,mi] += x[ni, d1, d2] * w[mi, d1, d2] 
    return cout