Webmargin: The cosine margin penalty (m in the above equation). The paper used values between 0.25 and 0.45. scale: This is s in the above equation. The paper uses 64. Other info: This also extends WeightRegularizerMixin, so it accepts weight_regularizer, weight_reg_weight, and weight_init_func as optional arguments. This loss requires an … Web京东JD.COM图书频道为您提供《深度强化学习算法与实践：基于PyTorch的实现 张校捷著,张校 电子工业出版社》在线选购，本书作者：，出版社：电子工业出版社。买图书，到京东。网购图书，享受最低优惠折扣!

Multilabelmarginloss - PyTorch Forums

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Choosing a margin for contrastive loss in a siamese network

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