It is useful when training a classification problem with C classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. This is …

Sep 17, 2024 · Categorical Cross-Entropy (CCE), also known as softmax loss or log loss, is one of the most commonly used loss functions in machine learning, particularly for classification …

Dec 1, 2021 · This post is the most important post in the fourth chapter. Here we will talk about Categorical cross-entropy loss and what it means. First, in both answers, i.e. correct and …

CCE LOSS是大多数多分类问题的首选损失函数 3.2 Binary Cross-Entropy. BCE损失函数的定义如下： 优点： 适用于多标签问题和二元分类; BCE损失函数的工作原理类似于类别交叉熵损失函 …

Jan 3, 2024 · Cross-entropy loss also known as log loss is a metric used in machine learning to measure the performance of a classification model. Its value ranges from 0 to 1 with lower …

May 23, 2018 · In this post I group up the different names and variations people use for Cross-Entropy Loss. I explain their main points, use cases and the implementations in different deep …

May 27, 2024 · Binary Cross-Entropy (BCE) is a crucial loss function for binary classification tasks, effectively measuring the performance of models by comparing true labels with …

We propose Cut Cross-Entropy (CCE), a method that computes the cross-entropy loss without materializing the logits for all tokens into global memory. Rather, CCE only computes the logit …

Sep 8, 2023 · Categorical Cross-Entropy (CCE), often referred to as softmax cross-entropy or simply cross-entropy loss, is a loss function commonly used in multi-class classification tasks.

May 20, 2018 · Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE. Proposed loss functions can be readily …