ψ(θ)首先出现在公式 (4) 中：(4)Li=−log(e‖Wyi‖‖xi‖ψ(θyi)e‖Wyi‖‖xi‖ψ...
PyTorch(>=1.0.0) Training The softmax loss with the large-margin regularization can be simply incorporated by frommodels.modules.mylossimportLargeMarginInSoftmaxLosscriterion=LargeMarginInSoftmaxLoss(reg_lambda=0.3) wherereg_lambdaindicates the regularization parameter. ...
Various loss functions for softmax variants: center loss, cosface loss, large-margin gaussian mixture, COCOLoss implemented by pytorch 0.3.1 the training dataset is MNIST You can directly run code train_mnist_xxx.py to reproduce the result The reference papers are as follow: Center loss: Yandon...
然后,rank regularization模块将这些权重按降序排列,将它们分成两组(即高重要度权重和低重要度权重),并通过在两组的平均权重之间设置一个margin来对这两组进行正则化。这种正则化是通过一个损失函数来实现的,称为Rank Regularization loss (RR-Loss)。rank regularization模块确保第一个模块学习有意义的权值来突出某些样...
CosFace: Large Margin Cosine Loss(MLCL) for Deep Face Recognition 下载地址:https://arxiv.org/pdf/1801.09414.pdf 论文中的cos loss: cos loss 的 TF 实现: 代码语言:javascript 复制 # coding=utf-8importtensorflowastfimportnumpyasnp defpy_func(func,inp,Tout,stateful=True,name=None,grad_func=None...
BloombergGPT是一个使用标准左到右因果语言建模目标的PyTorch模型。遵循Brown等人(2020)的做法,我们希望所有的训练序列长度完全相同,在我们的案例中是2048个标记,以最大化GPU利用率。为了实现这一点,我们将所有分词的训练文档与一个`<|endoftext|>`标记作为文档分隔符连接起来。然后,我们将这个标记序列分成2048个标记...
Here for Lcls we use a two class softmax loss, while for Lreg we use the smooth L1 loss. 4.3. Operation Network After getting approaching vectors from graspable points, we further predict in-plane rotation, approaching distance, gripper width and grasp co...
为了提高GPU的利用率,FasterTransformer手动将注意计算(例如,线性投影、位置偏移、点积和softmax等)融合到单个高性能核模板中,并涉及多种核优化技术,如碎片内存缓存、用于降低的warp-shuffle指令,具有张量核和多精度支持的半矩阵乘法和累加(HMMA,half matrix multiplication and accumulation)。FlexFlow Serve支持试探(...
pytorch_loss: loss for training - label-smooth - amsoftmax - focal-loss - dual-focal-loss - triplet-loss - giou-loss - affinity-loss - pc_softmax_cross_entropy - ohem-loss(softmax based on line hard mining loss) - large-margin-softmax(bmvc2019) - lovasz-softmax-loss - dice-loss(...
Although I'm using version 1.9, I think the problem still exists on master. I think the problem might be in this line of code:https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/cuda/SoftMax.cu#L667. When dim_size is too large for int, it becomes negative. May I ask...