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。 weig
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...
During training, the binary cross-entropy loss of the predicted outputs for a single batch is calculated as follows: $$J\left(w\right)=-\frac{1}{N}\sum_{n=1}^{N}[{y}^{n}log\left(f\left({x}_{i}^{n};w\right)\right){\widehat{y}}^{n}+(1-{y}^{n})\mathrm{log}(1-...
For the pseudo-labeling framework, five GMF models were tuned using an Adam optimizer and 0.02 learning rate to minimize the cross-entropy loss with 50 epochs at each step. Then the pseudo-labels were marked and added to the training set. The threshold for labeling pseudo-labels was set to ...
Since the objective function for each task is a cross-entropy loss, which is defined as:(3)L(y^,y)=−∑iyilog(y^i) The total loss function of a MTL model with random-weighted loss, therefore, can be calculated as:(4)Ltotal(y^1...y^k,y1...yk)=∑j=1k(−∑iyijlog(y^...
Then, the binary particle swarm improved SVM is utilized for diagnosis. Sun et al. [3] extracted the energy entropy decomposing by wavelet packet in the acoustic signal of the switch machine, and then used the particle swarm improved SVM for diagnosis. However, the signal processing-based ...
3)损失函数:为了训练我们提出的模型,我们选择binary cross entropy作为分割损失函数 C. patch重组 在测试阶段,我们没有像在训练阶段那样做随机的重叠patch裁剪,我们只是把512×512的输入平铺到8×8个大小为64×64的不重叠patch上。这样,在得到每个分割块的预测后,我们可以根据每个分割块的位置重新分组得到整个眼底图像...
We deploy the gated cross-attention unit in third and fourth layer of encoder where the feature maps have 1/81/8 and 1/161/16 sizes of the original input images, respectively. The segmentation loss 𝐿𝑠𝑒𝑔Lseg is the sum of the binary cross-entropy (BCE) loss and Dice loss, ...
We then use the cross-entropy loss function to train the classification network: 𝐿𝑐𝑙𝑠=−[𝑦⋅log(𝑦̂)+(1−𝑦)⋅log(1−𝑦̂) ]Lcls=−[y⋅logy^+1−y⋅log(1−y^) ] (6) 4. Experiment In this section, we design experiments to demonstrate...
In addition to the cross-entropy loss, we use the weight-generation loss in the joint loss function for our proposed method, with the aim of maximizing the gap between different classes by guiding the weight generation module. Comparative experiments on the MSTAR and VA datasets demonstrate that...