在pytorch中,对应的函数为torch.nn.BCELossWithLogits和torch.nn.BCELoss https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a
简介: binary_cross_entropy和binary_cross_entropy_with_logits都是来自torch.nn.functional的函数 binary_cross_entropy和binary_cross_entropy_with_logits都是来自torch.nn.functional的函数,首先对比官方文档对它们的区别: 函数名 解释 binary_cross_entropy Function that measures the Binary Cross Entropy between ...
>>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward() """ if has_torch_function_variadic(input, target, weight, pos_weight): return handle_torch_function( binary_cross_entropy_with_logits, (input, target, weight, pos_weight), input, target, weight=weight, ...
Pytorch's single binary_cross_entropy_with_logits function. F.binary_cross_entropy_with_logits(x, y) out: tensor(0.7739) __EOF__ 本文来自博客园,作者:SXQ-BLOG,转载请注明原文链接:https://www.cnblogs.com/sxq-blog/p/17068865.html 分类:DeepLearning ...
pytorch binary_cross_entropy 多分类 如何使用逻辑回归 (logistic regression)来解决多类别分类问题 第一个例子:假如说你现在需要一个学习算法能自动地将邮件归类到不同的文件夹里,或者说可以自动地加上标签,那么,你也许需要一些不同的文件夹,或者不同的标签来完成这件事,来区分开来自工作的邮件、来自朋友的邮件、...
loss = self.binary_cross_entropy(logits, labels, weight) return loss 通过源码我们可以看出,BCELoss实际上是对BinaryCrossEntropy的一层封装(weight为None代表各个样本权重值相同)。 2.2 实例验证 下面我们通过一个实例来验证源码解析部分的结论。 实例中我们将weight设置1.0,即各个样本权重相同,等价于BCELoss中参数...
sigmoid和softmax是神经网络输出层使用的激活函数，分别用于两类判别和多类判别。binary cross-entropy和...
PyTorch Binary cross entropy loss function In this section, we will learn about thePyTorch cross-entropy loss functionin python. Binary cross entropy is a loss function that compares each of the predicted probabilities to actual output that can be either 0 or 1. ...
Outline & Motivation In lightning/examples/pytorch/domain_templates/computer_vision_fine_tuning.py file, class TransferLearningModel(LightningModule): use F.binary_cross_entropy_with_logits as the loss function, however the output of sel...
-For a binary classification problem->binary\_crossentropy