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
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。
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...
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 ...
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 ...
Introducing the cross-entropy cost functionHow can we address the learning slowdown? It turns out that we can solve the problem by replacing the quadratic cost with a different cost function, known as the cross-entropy. To understand the cross-entropy, let's move a little away from our ...
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...
When the forecast is wrong, the loss function returns an abnormally large number, whereas when the prediction is relatively accurate, it returns a little value. This article uses the cross-entropy loss function, whose formula is as follows(12)L=1N∑iLi=−1N∑i∑c=1Myiclg(pic),where M ...
The reasons why PyTorch implements different variants of the cross entropy loss are convenience and computational efficiency. Remember that we are usually interested in maximizing the likelihood of the correct class. Maximizing likelihood is often reformulated as maximizing the log-likelihood, because takin...
Objective function The proposed model is implemented using a supervised setting. The over-fitting of the model is addressed in this instance using the L2 regularization procedure. The loss function applied in this scenario is the cross-entropy loss shown in Eq. (8). In this instance, the predi...