CosineEmbeddingLoss

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

建立了一個標準，用於衡量輸入張量 $x_1$ 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

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

引數

• 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) – 已棄用 (參見 reduction)。預設情況下，損失值在批次中的每個損失元素上取平均值。請注意，對於某些損失，每個樣本有多個元素。如果欄位 size_average 設定為 False，則損失值在每個小批次中而是求和。當 reduce 為 False 時忽略。預設值：True

• reduce (bool, optional) – 已棄用 (參見 reduction)。預設情況下，損失值在每個小批次中根據 size_average 對觀測值進行平均或求和。當 reduce 為 False 時，返回每個批次元素的損失值，並忽略 size_average。預設值：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'

形狀

• 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)

執行前向傳播。

返回型別

張量