train_gan.py 文件源码

def loss_dis(self, dis, dis_real, dis_fake):
        batchsize,_,w,h = dis_real.data.shape

        L1 = (2+np.random.rand()) * F.sum(F.softplus(-dis_real)) / batchsize / w / h
        L2 = (2+np.random.rand()) * F.sum(F.softplus(dis_fake)) / batchsize / w / h
        loss =  L1 + L2 
        chainer.report({'loss': loss}, dis)
        return loss