对于每个类别的mask,都计算一个 Dice 损失: 将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6):'''Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.Assumes the...
Focal Loss for Dense Object Detection focal loss的设计很巧妙,就是在cross entropy的基础上加上权重,让模型注重学习难以学习的样本,训练数据不均衡中占比较少的样本,相对放大对难分类样本的梯度,相对降低对易分类样本的梯度,并在一定程度上解决类别不均衡问题。 如果将cross loss定义为: 那focal loss加权后...
On the other hand, most studies use categorical cross-entropy loss function, which is not optimal for the ordinal regression problem, to train the deep learning models. In this study, we propose a novel loss function called class distance weighted cross-entropy (CDW-CE) that respects the ...
求导,得 可以看出,当权重为1时就是不加权的Loss。 二、实现Python SigmoidCrossEntropyWeightLossLayer import caffe import numpy as npclassSigmoidCrossEntropyWeightLossLayer(caffe.Layer):defsetup(self,bottom,top):# check for all inputsparams=eval(self.param_str)self.cls_weight=float(params["cls_weight"...
The exponentially weighted cross-entropy (EWCE) loss function is designed to address the problem of inaccurate recognition of small-scale imbalanced underwater acoustic datasets. Compared with the cross-entropy loss, the EWCE loss down-weights the loss of the correctly predicted samples and focuses on...
Recognition of imbalanced underwater acoustic datasets with exponentially weighted cross-entropy loss Class imbalance, an objective problem of underwater acoustic datasets, has hardly been paid attention to, but often results in low recognition accuracy of ......
The PyTorch code for paper: An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss The model is largely based on OpenNMT-py(v0.1) and PyTorch 0.4. Steps Download data from OpenSubtitles, Cornell Movie Dialog Corpus, DailyDialog datasets or your own datasets...
I've implemented an analog of weighted_cross_entropy_with_logits in my current project. It's useful for working with imbalanced datasets. I want to add it to PyTorch but I'm in doubt if it is really needed for others. For example, my imp...
一、cross entropy loss 二、weighted loss 三、focal loss 四、dice soft loss 五、soft IoU loss 总结: 一、cross entropy loss 用于图像语义分割任务的最常用损失函数是像素级别的交叉熵损失,这种损失会逐个检查每个像素,将对每个像素类别的预测结果(概率分布向量)与我们的独热编码标签向量进行比较。
简介:损失函数大全Cross Entropy Loss/Weighted Loss/Focal Loss/Dice Soft Loss/Soft IoU Loss 一、crossentropyloss 用于图像语义分割任务的最常用损失函数是像素级别的交叉熵损失,这种损失会逐个检查每个像素,将对每个像素类别的预测结果(概率分布向量)与我们的独热编码标签向量进行比较。