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Loss Info
loss 的具体形式取决于机器学习任务的类型。例如,在回归问题中,常用的 loss 函数包括平方损失、绝对损失和对数损失;在分类问题中,常用的 loss 函数包括交叉熵损失和 Hinge 损失。 accuracy 是指模型的准确率,即模型预测结果与真实结果的匹配度。
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深度学习中loss和accuracy的关系? - 知乎
loss 的具体形式取决于机器学习任务的类型。例如,在回归问题中,常用的 loss 函数包括平方损失、绝对损失和对数损失;在分类问题中,常用的 loss 函数包括交叉熵损失和 Hinge 损失。 accuracy 是指模型的准确率,即模型预测结果与真实结果的匹配度。
神经网络模型train_loss下降,val_loss一直上升是什么原因? - 知乎
知乎,中文互联网高质量的问答社区和创作者聚集的原创内容平台,于 2011 年 1 月正式上线,以「让人们更好的分享知识、经验和见解,找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容,聚集了中文互联网科技、商业、影视 ...
求通俗易懂解释下nce loss? - 知乎
选择哪种Loss? NCE Loss和Sampled Softmax Loss在召回中都有广泛运用. 从word2vec派生出来的算法,如Airbnb和阿里的EGES召回,都使用的是NCE Loss。准确一点说,是NCE的简化版,NEG Loss。尽管NEG Loss在理论上无法等价原始的超大规模softmax,但是不妨碍学习出高质量的embedding。
深度学习的loss一般收敛到多少? - 知乎
看题主的意思,应该是想问,如果用训练过程当中的loss值作为衡量深度学习模型性能的指标的话,当这个指标下降到多少时才能说明模型达到了一个较好的性能,也就是将loss作为一个evaluation metrics。 但是就像知乎er们经常说的黑话一样,先问是不是,再问是什么。
有哪些「魔改」loss函数,曾经拯救了你的深度学习模型? - 知乎
回答下正题,loss function还是很容易改的,有次我需要更改我的mini batch的loss 权重,就可以使用pytorch 的reduce功能,得到矩阵以后,直接加权。效果还不错。当然我这个和focal loss并不是一类问题。 感觉还是根据自己的需要去更改自己的loss才最好。
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