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 loss和交叉熵损失（Cross-Entropy Loss）是相关但不完全相同的概念。
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...
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...
CrossEntropy 交叉熵推导 1. 引言 我们都知道损失函数有很多种:均方误差(MSE)、SVM的合页损失(hinge loss)、交叉熵(cross entropy)。这几天看论文的时候产生了疑问:为啥损失函数很多用的都是交叉熵(cross entropy)?其背后深层的含义是什么?如果换做均方误差(MSE)会怎么样?下面我们一步步来揭开交叉熵的神秘面纱。
Additionally, we examine the equilibrium coefficients of each branch loss function, represented by αi and Li(ui,vi), where i ranges from 1 to 4. Moreover, we analyze the cross-entropy loss function. For the purpose of model training, we set the equilibrium coefficients as follows: [β,...
2. Lower Bounds on Cross-Entropy Loss 本节描述了一种框架来计算在对抗攻击下交叉熵损失的下界。该框架可用于普通离散分布和两种高斯混合分布下下对于带有对抗扰动样本的二分类问题。 2.1 问题描述 x表示X分部空间下的输入样本图片,y=1或-1,表示二分类下的标签,而f描述x到y的映射关系,即分类函数。这里作者引入...
For the loss function we usually use MSE for linear layers or cross-entropy for softmax layers such that the backpropagated error becomes the difference of the prediction and the target. I suggest for a detailed understanding to study the topic in the deep learning book by Goodfellow et al....
Logit (Zhao et al., 2021) Replace the cross-entropy loss with logit loss Logit-Margin (Weng et al., 2023) Downscale the logits using a temperature factor and an adaptive margin CFM (Byun et al., 2023) Mix feature maps of adversarial examples with clean feature maps of benign images sto...
loss=dict(type='CrossEntropyLoss', loss_weight=2 * temperature), temperature=temperature)) optimizer optim_wrapper = dict( type='AmpOptimWrapper', loss_scale='dynamic', optimizer=dict(type='LARS', lr=4.8, weight_decay=1.5e-6, momentum=0.9), ...