CosineEmbeddingLoss

class torch.nn.modules.loss.CosineEmbeddingLoss(margin=0.0, size_average=None, reduce=None, reduction='mean')

创建了一个标准，用于衡量输入张量 $x_1$ and $x_2$ and a Tensor label $y$ with values 1 or -1. Use ($y=1$) to maximize the cosine similarity of two inputs, and ($y=-1$) otherwise. This is typically used for learning nonlinear embeddings or semi-supervised learning.

The loss function for each sample is

$$\text{loss}(x, y) = \begin{cases} 1 - \cos(x_1, x_2), & \text{if } y = 1 \\ \max(0, \cos(x_1, x_2) - \text{margin}), & \text{if } y = -1 \end{cases}$$

参数

• margin (float, optional) – Should be a number from $-1$ to $1$, $0$ to $0.5$ is suggested. If margin is missing, the default value is $0$.

• size_average (bool, optional) – 已弃用 (参见 reduction)。默认情况下，损失值在批次中的每个损失元素上取平均值。请注意，对于某些损失，每个样本有多个元素。如果字段 size_average 设置为 False，则损失值在每个小批次中而是求和。当 reduce 为 False 时忽略。默认值：True

• reduce (bool, optional) – 已弃用 (参见 reduction)。默认情况下，损失值在每个小批次中根据 size_average 对观测值进行平均或求和。当 reduce 为 False 时，返回每个批次元素的损失值，并忽略 size_average。默认值：True

• reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

形状

• Input1: $(N, D)$ or $(D)$, where N is the batch size and D is the embedding dimension.

• Input2: $(N, D)$ or $(D)$, same shape as Input1.

• Target: $(N)$ or $()$.

• Output: If reduction is 'none', then $(N)$, otherwise scalar.

示例

>>> loss = nn.CosineEmbeddingLoss()
>>> input1 = torch.randn(3, 5, requires_grad=True)
>>> input2 = torch.randn(3, 5, requires_grad=True)
>>> target = torch.ones(3)
>>> output = loss(input1, input2, target)
>>> output.backward()

forward(input1, input2, target)

执行前向传播。

返回类型

张量