learning.py 文件源码

def create_minibatch_indices(n, minibatch_size, shuffling=True):
    """
    :param n: total number of indices from which to pick from
    :param minibatch_size: size of the minibatches (must be lower than n)
    :return: (list of random indices, number of random duplicate indices in the last minibatch to complete it)
    """
    if shuffling:
        all_indices = np.random.permutation(n)  # shuffle order randomly
    else:
        all_indices = np.arange(n)
    n_steps = (n - 1) // minibatch_size + 1  # how many batches fit per epoch
    n_rem = n_steps * minibatch_size - n  # remainder
    if n_rem > 0:
        inds_to_add = np.random.randint(0, n_rem, size=n_rem)
        all_indices = np.concatenate((all_indices, inds_to_add))
    return np.split(all_indices, n_steps), n_rem