11 2分类的logistic损失 SoftMarginLoss torch.nn.SoftMarginLoss(reduction='mean') 12 多标签 one-versus-all 损失 MultiLabelSoftMarginLoss torch.nn.MultiLabelSoftMarginLoss(weight=None, reduction='mean') 13 cosine 损失 CosineEmbeddingLoss torch.nn.CosineEmbeddi...
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合页损失 Hinge Loss 是另外一种二分类损失函数,适用于 maximum-margin 的分类,支持向量机 Support Vector Machine (SVM) 模型的损失函数本质上就是 Hinge Loss + L2 正则化。合页损失的公式如下 下图是 为正类, 即时,不同输出的合页损失示意图 可以看到当 为正...
2.1.49 Part 3 Section 5.6, text:soft-page-break 2.1.50 Part 3 Section 6.1.2, White Space Characters 2.1.51 Part 3 Section 6.1.3, text:s 2.1.52 Part 3 Section 6.1.4, text:tab 2.1.53 Part 3 Section 6.1.5, text:line-break 2.1.54 Part 3 Section 6.1.7, text:span 2.1....
通过学习,让正样本特征表达之间的距离尽可能小,而负样本的特征表达之间的距离尽可能大,并且要让正样本之间的距离和负样本之间的距离之间有一个最小的间隔(margin)。 损失函数如下所示: Sum Hinge Loss & Max Hinge Loss Triplet loss 的输入是 (a, p, n),一般的做法是 b 个 ...
使用动态的学习率衰减lr_scheduler=paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=cfg.lr_decay,T_max=cfg.max_epochs,eta_min=0.0)# loss_fn = focal_loss # 感觉使用这个损失函数的效果会更好loss_fn=margin_losstrain(model,train_data_loader,val_data_loader,optimizer,lr_scheduler,loss_fn,...
542 Max-Margin DeepWalk: Discriminative Learning of Network Representation Cunchao Tu; Weicheng Zhang; Zhiyuan Liu; Maosong Sun IJCAI 2016 25 543 Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems Clemens Eppner; Sebastian Höfer; Rico Jonschkowski; Roberto Martín-...
总结一下,这里面需要注意的应该就是对于different的两个图片,需要设置一个margin,然后小于margin的计算损失,大于margin的损失为0. 3.2 Contrastive Loss pytorch # Custom Contrastive Loss class ContrastiveLoss(torch.nn.Module): """ Contrastive loss function. ...
logsigmoid(rewards_chosen - rewards_rejected - inputs["margin"]).mean() else: loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean() 2、PPOTrainer 2.1 训练流程概览 由3个阶段组成,分别是:Rollout、Evaluation和Optimization。更多详情见:RHLF01 - PPO原理介绍 (1)Rollout: 根据...