损失函数(Loss functions) Vision functions) Convolution 函数 torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) 对由几个输入平面组成的输入信号应用一维卷积。 详细信息和输出形状,查看Conv1d 参数: ...
Got different results when running compiled version of torch.nn.functional.multilabel_margin_loss. Reproducer: import torch dtype = torch.float32 C = 6 N = 2 reduction = "none" #backend = "eager" # this works backend = "aot_eager" # this fails def func(x, y, reduction): result = ...
multilabel_margin_loss¶ torch.nn.functional.multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean')→ Tensor[source]¶ See MultiLabelMarginLoss for details. multilabel_soft_margin_loss¶ torch.nn.functional.multilabel_soft_margin_loss(input, target, weight=...
torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)→ Tensorsource在由几个输入平面组成的输入图像上应用2D转置卷积,有时也被称为去卷积。 有关详细信息和输出形状,参考ConvTranspose2d。
multilabel_margin_losstorch.nn.functional.multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor [source] See MultiLabelMarginLoss for details.multilabel_soft_margin_losstorch.nn.functional.multilabel_soft_margin_loss(input, target, weight=None, size_...
The following are 7 code examples of torch.nn.functional.multilabel_soft_margin_loss(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out ...
🚀 The feature, motivation and pitch I'm working on a metric to measure the efficiency of a neural network. My approach requires computing per-example gradient using multi_margin_loss. Like evaluating the test accuracy of a model, the mor...
output = loss(input, target) output.backward() 3 CrossEntropyLoss 交叉熵损失函数 交叉熵损失函数=nn.LogSoftmax()+nn.NLLLoss() 因为神经网络输出的是向量,并不是概率分布的形式。所以需要 softmax激活函数将一个向量进行“归一化”成概率分布的形式,再采用交叉熵损失函数计算 loss。
torch.nn.functional.margin_ranking_loss Supported 84 torch.nn.functional.multilabel_margin_loss Unsupported. 85 torch.nn.functional.multilabel_soft_margin_loss Supported 86 torch.nn.functional.multi_margin_loss Unsupported. 87 torch.nn.functional.nll_loss ...
torch.nn.MultiLabelMarginLoss Unsupported. 174 torch.nn.SmoothL1Loss Supported 175 torch.nn.SoftMarginLoss Unsupported. 176 torch.nn.MultiLabelSoftMarginLoss Supported 177 torch.nn.CosineEmbeddingLoss Supported 178 torch.nn.MultiMarginLoss Unsupported. 179 torch.nn.TripletMarginLoss ...