在PyTorch中,可以使用torch.nn.functional中的weighted_cross_entropy_with_logits函数来模拟加权交叉熵损失函数。 加权交叉熵损失函数是一种常用的用于多分类问题的损失函数,它可以解决类别不平衡问题。在实际应用中,不同类别的样本数量可能存在差异,为了平衡不同类别的重要性,可以使用加权交叉熵损失函数。 weighted_cross...
I've implemented an analog of weighted_cross_entropy_with_logits in my current project. It's useful for working with imbalanced datasets. I want to add it to PyTorch but I'm in doubt if it is really needed for others. For example, my imp...
手把手教你用Pytorch-Transformers——部分源码解读及相关说明(一) 2019-11-27 20:57 −简介 Transformers是一个用于自然语言处理(NLP)的Python第三方库,实现Bert、GPT-2和XLNET等比较新的模型,支持TensorFlow和PyTorch。本文介对这个库进行部分代码解读,目前文章只针对Bert,其他模型看心情。 github:https://github....
在PyTorch中使用WeightedRandomSampler 是为了解决数据不平衡问题的一种采样方法。数据不平衡指的是训练集中不同类别的样本数量差异较大,这会导致模型对数量较多的类别更加偏向,而对数量较少的类别学习不足。 WeightedRandomSampler可以根据每个样本的权重来进行采样,使得每个样本被选择的概率与其权重成正比。这样可以保证每个...
图2 加权Res-UNet的总体结构 结果 image.png 参考链接: 基于U-Net+残差网络的语义分割缺陷检测 Keras 使用Residual-Block 加深U-net网络的深度 U-net与ResNet结合 基于Resnet+Unet的图像分割模型(by Pytorch) U-Net 和 ResNet:长短跳跃连接的重要性(生物医学图像分割)...
A camera classifier θc is trained to dis- criminate the target = T cameras with a cross entropy loss, the gradient of this loss is inverted with a Gradient Reversal Layer (GRL) and back-propagated through the encoder. This reversed objective aims to ...
All experiments were conducted using NVIDIA 1080ti GPUs with 11 GB of memory per GPU and all deep learning models were implemented using Pytorch (v.0.4.1). We performed stratified fivefold cross-validations to distribute samples equally by considering class balance between the training set and val...
We propose a weakly supervised approach to semantic segmentation using bounding box annotations. Bounding boxes are treated as noisy labels for the foreground objects. We predict a per-class attention map that saliently guides the per-pixel cross entropy
The code programming of the model is implemented on the Pytorch platform. Algorithm 1: Weighted Subdomain Adaptation Network (WSAN) Model: Feature generator G; Auxiliary classifier CA; Classifier C. Input: Labeled source data {Xs, Cs} and unlabeled target data Xt. For i in epochs: Step 1...
To ensure a fair comparison, we replicated a portion of the network using the PyTorch framework and compared our results with those reported in typical published papers. Our experiments were conducted on a computer with an 11th Gen Intel(R) Core (TM) i7-1165G7 processor @ 2.80 GHz, 16 GB...