CosineSimilarity PairwiseDistance Loss functions L1Loss MSELoss CrossEntropyLoss CTCLoss NLLLoss PoissonNLLLoss KLDivLoss BCELoss BCEWithLogitsLoss MarginRankingLoss HingeEmbeddingLoss MultiLabelMarginLoss SmoothL1Loss SoftMarginLoss MultiLabelSoftMarginLoss CosineEmbeddingLoss MultiMarginLoss TripletMarginLoss ...
>>>input1 = autograd.Variable(torch.randn(100,128))>>>input2 = autograd.Variable(torch.randn(100,128))>>>output = F.cosine_similarity(input1, input2)>>>print(output) 损失函数(Loss functions) torch.nn.functional.nll_loss(input, target, weight=None, size_average=True) ...
()torch.nn.functional.cosine_embedding_loss()torch.nn.functional.cosine_similarity()torch.nn.functional.cross_entropy()torch.nn.functional.ctc_loss()torch.nn.functional.dropout()torch.nn.functional.dropout2d()torch.nn.functional.dropout3d()torch.nn.functional.elu()torch.nn.functional.elu_()torch....
def __init__(self, model): super(MSELoss, self).__init__() self.model = model Example #22Source File: CosineSimilarityLoss.py From sentence-transformers with Apache License 2.0 5 votes def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor): reps = [self....
>>> input1 = autograd.Variable(torch.randn(100, 128)) >>> input2 = autograd.Variable(torch.randn(100, 128)) >>> output = F.cosine_similarity(input1, input2) >>> print(output)损失函数(Loss functions)torch.nn.functional.nll_loss(input, target, weight=None, size_average=True)...
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。 主要参数:
torch.nn.functional.cosine_embedding_loss(input1, input2, target, margin=0, size_average=True, reduce=True)→ Tensorsource详细可见CosineEmbeddingLosstorch.nn.functional.cross_entropy(input, target, weight=None, size_average=True, ignore_index=-100, reduce=True)...
torch.nn.CosineSimilarity Unsupported. 161 torch.nn.PairwiseDistance Supported 162 torch.nn.L1Loss Supported 163 torch.nn.MSELoss Supported 164 torch.nn.CrossEntropyLoss Supported 165 torch.nn.CTCLoss Unsupported. 166 torch.nn.NLLLoss Supported 167 torch.nn.PoissonNLLLoss Unsup...
torch.nn.CosineSimilarity Unsupported. 161 torch.nn.PairwiseDistance Supported 162 torch.nn.L1Loss Supported 163 torch.nn.MSELoss Supported 164 torch.nn.CrossEntropyLoss Supported 165 torch.nn.CTCLoss Unsupported. 166 torch.nn.NLLLoss Supported 167 torch.nn.PoissonNLLLoss Unsup...
不回9.paddle.nn.HSigmoidLoss() 层次sigmoid损失层 不回paddle.nn.CTCLoss() CTCLoss层 十三、Vision层 十四、Clip相关 paddle.nn.ClipGradByGlobalNorm 将一个 Tensor列表 t_list 中所有Tensor的L2范数之和,限定在 clip_norm 范围内 paddle.nn.ClipGradByNorm() 将输入的多维Tensor X 的L2范数限制在 clip...