compute_cost.py

def compute_cost(Al, Y):
    
    """
    Arguments:
    AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
    Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)

    Returns:
    cost -- cross-entropy cost
    """
    
    m = Y.shape[1]
    
    cost = -1/m * np.sum(np.multiply(Y, np.log(Al)) + np.multiply((1-Y), np.log(1-Al)))
    
    cost = np.squeeze(cost)
    
    return cost