CrossEntropyLoss

CrossEntropyLoss 是一个在分类问题中广泛使用的损失函数，它最小化模型的预测概率和真实标签的差异。它是交叉熵和负对数似然损失函数的组合。在训练过程中，交叉熵损失函数帮助优化神经网络的参数，从而最小化分类错误。

相关问题

Cross Entropy Loss

Cross Entropy Loss is a commonly used loss function in machine learning, especially in classification tasks. It measures the dissimilarity between the predicted probability distribution and the actual probability distribution of the target class. The formula for calculating cross entropy loss is often defined as follows:

L(y, ŷ) = -Σ(yi * log(ŷi))

where y is the true probability distribution and ŷ is the predicted probability distribution. This loss function penalizes the model more heavily for making larger errors in predicting the true class probabilities.

cross entropy loss

Cross entropy loss是一种常用的损失函数，通常用于分类问题中。它的计算方式是将真实标签的概率分布与预测标签的概率分布进行比较，得到一个衡量两者差异的值。

具体来说，假设有N个样本，每个样本有K个类别，真实标签为one-hot向量$y_i$，预测标签为概率分布向量$\hat{y_i}$，则交叉熵损失函数的计算公式为：

$$
L = -\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{K}y_{i,j}\log{\hat{y_{i,j}}}
$$

其中，$y_{i,j}$表示第i个样本属于第j个类别的真实标签值，$\hat{y_{i,j}}$表示第i个样本属于第j个类别的预测概率值。

交叉熵损失函数的优点是能够有效地惩罚预测结果与真实结果之间的差异，从而提高模型的分类准确率。