utils.py 文件源码

def generate(rbm, iteration = 1, p = 0.5, n = 1):

    v = torch.bernoulli((rbm.v_bias *0 + p).view(1,-1).repeat(n, 1))
    for _ in range(iteration):

        p_h, h = rbm.v_to_h(v)

        p_v, v = rbm.h_to_v(h)
    return v