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计算机视觉的图像L2损失函数，一般收敛到多少时，效果就不错了呢？ 如何设计loss函数？ Loss函数和你任务的评价准则越相关，二者越接近越好。 如果你任务的评价准则是F1-score（不可导），但一直在使用CrossEntropy Loss来迭代模型，二者之间虽然相关性很高但仍存在非线性。 如何在Pytorch中使用loss函数？ 深度学习的loss一般收敛到多少？ 计算机视觉的图像L2损失函数，一般收敛到多少时，效果就不错了呢？ 显示全部 关注者 111
类似的Loss函数还有IoU Loss。 如果说DiceLoss是一种 区域面积匹配度 去监督网络学习目标的话，那么我们也可以使用 边界匹配度去监督网络的Boundary Loss。 我们只对边界上的像素进行评估，和GT的边界吻合则为0，不吻合的点，根据其距离边界的距离评估它的Loss。 深度学习当中train loss和valid loss之间的关系？ 深度学习当中train loss和valid loss之间的关系，在一个caption实验当中，使用交叉熵作为损失函数，虽然随着训练，模型的评价指标的… 显示全部 关注者 35 牛津高阶上，给出的用法是be at a loss for words 和i'm at a loss what to do next
8本电子书免费送给大家，见文末。 常见的 Loss 有很多，比如平方差损失，交叉熵损失等等，而如果想有更好的效果，常常需要进行loss function的设计和改造，而这个过程也是机器学习中的精髓，好的损失函数既可以反映模型的训练误差，也可以反映模型的泛化误差，可参考以下几种思路： 首先就是.
多个loss引入 pareto优化理论，基本都可以涨点的。 例子： Multi-Task Learning as Multi-Objective Optimization 可以写一个通用的class用来优化一个多loss的损失函数，套进任何方法里都基本会涨点。反正我们在自己的研究中直接用是可以涨的。 Tensorflow实现了两种常用与word2vec的loss，sampled softmax和NCE，这两种loss本身可以用于任意分类问题。 之前一直不太懂这两种方法，感觉高深莫测，正好最近搞懂了，借tensorflow的代码和大家一起分享一下我的理解，也记录一下思路。 上图就是一个很典型的过拟合现象，训练集的 loss 已经降到0了，但是验证集的 loss 一直在上升，因此这不是一个很好的模型，因为它太过拟合了。 如果我们非要用这个模型，应该在5~10代的时候停止训练，这个操作叫提前停止，是正则化方法之一。
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