train_gan.py 文件源码

def loss_cnn(self, cnn, x_out, dst, dis_out, lam1=100, lam2=1):
        loss_rec = lam1 * ( F.mean_absolute_error(x_out, dst) )
        batchsize,_,w,h = dis_out.data.shape
        loss_adv = lam2 * F.sum( F.softplus(-dis_out) ) / batchsize / w / h

        loss = loss_rec + loss_adv 
        chainer.report({'loss': loss,"loss_rec":loss_rec, 'loss_adv': loss_adv }, cnn)        

        return loss