cross-entropy loss是一个损失函数，它衡量了预测概率和真实标签之间的误差。
The Cross-Entropy Loss Function for the Softmax Function 作者:凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 本文介绍含有softmax函数的交叉熵损失函数的求导过程,并介绍一种交叉熵损失的
softmax函数用于将任意实数向量转换为概率值，确保结果之和为1且位于0-1之间。分类交叉熵损失衡量预测概率与实际标签间的差异，专用于多类分类任务。在多类分类问题中，每个样本只属于一个类。交叉熵接受两个离散概率分布作为输入，输出表示两个分布相似度的数值。该损失函数在多类分类任务中，利用softmax...
Softmax function has 2 nice properties: Each value ranges between 0 and 1 The sum of all values is always 1 This makes it a really nice function to model probability distributions. We can understand Cross-Entropy loss from the perspective of KL divergence if we keep the following two things...
Note the main reason why PyTorch merges the log_softmax with the cross-entropy loss calculation in torch.nn.functional.cross_entropy is numerical stability. It just so happens that the derivative of the loss with respect to its input and the derivative of the log-softmax with respect to its...
Analysis with the Cross-Entropy Loss 根据交叉熵损失函数,目标是希望最大化期望条件对数似然 E_{p}[logp(y|x)] 。假设K分类器经过softmax得到的概率分布为p(y|x), 给定分类器 f_{s} 和f_{d} ,有以下参数关系 在学习分类器 f_{d} 的过程中,作者想要与传统的使用 p_{d}(y|x) 最大似然估计 进...
Moreover, we analyze the cross-entropy loss function. For the purpose of model training, we set the equilibrium coefficients as follows: [Math Processing Error][β,α1,α2,α3,α4]=[0.1,1,0.2,0.2,0.2]. This paper presents the configuration of the experimental environment, which includes ...
4、cross entropy loss交叉熵:主要是指整体预测的不确定性,即熵的概念,熵的值越大,说明其确定性越低,概率分布越接近;熵的值越小,说明确定性越高,概率预测分布相差越大,越逼近极端的0或者1。 图123 5、交叉熵函数cross_entropy=softmax+log+null_loss函数 ...
We present the Tamed Cross Entropy (TCE) loss function, a robust derivative of the standard Cross Entropy (CE) loss used in deep learning for classification tasks. However, unlike other robust losses, the TCE loss is designed to exhibit the same training properties than the CE loss in noisele...
The cross-entropy loss function [Math Processing Error]LCE is used to train the output module to improve the prediction performance as our base loss function. We define p as the prediction probability, y as the true label, x as the input, θ as the parameters of the model, and ε as ...