train3_unpaired.py 文件源码

def loss_dis(self, dis, D_p_rough, p_line, D_u_rough, u_line, batchsize, alpha=0.1, beta=0.1):
        xp = self.gen.xp
        loss_fake_p = F.softmax_cross_entropy(D_p_rough, Variable(xp.ones(batchsize, dtype=np.int32)))
        loss_real_p = F.softmax_cross_entropy(self.dis(p_line), Variable(xp.zeros(batchsize, dtype=np.int32)))
        loss_fake_u = F.softmax_cross_entropy(D_u_rough, Variable(xp.ones(batchsize, dtype=np.int32)))
        loss_real_u = F.softmax_cross_entropy(self.dis(u_line), Variable(xp.zeros(batchsize, dtype=np.int32)))
        loss = alpha * (loss_fake_p + loss_real_p) + beta * (loss_fake_u + loss_real_u)
        chainer.report({'loss': loss, 'fake_p': loss_fake_p, 'real_p':loss_real_p, 'fake_u': loss_fake_u, 'real_u':loss_real_u}, dis)
        return loss