在"wrapper"函数内部,先通过调用"loss_func"函数来计算每个元素的损失值,然后再通过调用"weight_reduce_loss"函数来应用权重和减少损失。最后,返回损失值。 最后,装饰器"weighted_loss"返回"wrapper"函数,因此当使用该装饰器时,"loss_func"函数将被加权并返回其加权版本。 weight_reduce_loss def weight_reduce_loss...
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。 假设我们需要...
weighted log loss出自YouTube 2016年的论文[1],基于交叉熵损失实现回归问题,对观看时长进行预估。 1 原理 回归问题常用MSE损失,但回归问题比较难学,为了缓解回归问题的学习难度,常用的方法是将回归问题转化为多分类问题,将预估目标划分为多段,预估每一段的概率,概率最大的那段对应的值则为预估的值。这种方法依赖...
这里介绍下focal loss的两个重要性质:1、当一个样本被分错的时候,pt是很小的(请结合公式2,比如当y=1时,p要小于0.5才是错分类,难分类样本,此时pt就比较小,反之亦然),因此调制系数就趋于1,也就是说相比原来的loss是没有什么大的改变的。当pt趋于1的时候(此时分类正确而且是易分类样本),调制系数趋于0,也就...
The implementation of the full weighted loss function L full-weighted , as well as other baseline loss functions, can be found in the losses.py file. Installation pip install -r requirements.txt Training a model with the full weighted loss function python train_model.py --region SWI How to...
Y. (2006), “Weighted-loss-function control charts,” Int J Adv Manuf Technol, 31, 107-115. :Zhang Wu,Yu Tian.Weighted-loss-function control charts[J]. The International Journal of Advanced Manufacturing Technology .2006(1-2)Wu Z, Tian Y. Weighted-loss-function control charts. ...
We also design a novel weighted loss function to give less penalization to partial videos that have small observation ratios. Extensive evaluations on the challenging UCF101 and HMDB51 datasets demonstrate that the proposed method outperforms state-of-the-art results without knowing the observation ...
Summary This pull request introduces new weighted loss functions to the PyTorch library: weighted_huber_loss, wmse_loss, and wmae_loss. These functions allow for precise control over the influence of each sample during training, important for imbalanced
In this paper, we proposed a weighted softmax loss function called confusion weighted loss to learn the relationship among the confusing categories. Firstly, we generate a similarity matrix based on the confusion matrix to illustrate the relationship among the categories. Then, we propose a ...
def full_weighted_loss(pred_x, y, pred_bg, species_weights): batch_size = pred_x.size(0) # loss at data location loss_dl_pos = (log_loss(pred_x) * y * species_weights.repeat((batch_size, 1))).mean() loss_dl_neg = (log_loss(1 - pred_x) * (1 - y) * (species_weigh...