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mindspore.nn.HingeEmbeddingLoss

class mindspore.nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')

Hinge Embedding Loss. Compute the output according to the input elements. Measures the loss given an input tensor x and a labels tensor y (containing 1 or -1). This is usually used for measuring the similarity between two inputs.

The loss function for $n$-th sample in the mini-batch is

$$\begin{array}{r}{l}_n=\{\begin{array}{ll}{x}_n,& \text{if}{y}_n=1,\\ max\{0,\mathrm{\Delta }-x_n\},& \text{if}{y}_n=-1,\end{array}\end{array}$$

and the total loss functions is

$$\begin{array}{r}\ell (x,y)=\{\begin{array}{ll}\mathrm{mean}(L),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

where $L=\{l_1,\dots ,l_N{\}}^{\mathrm{\top }}$.

Parameters

• margin (float) – Threshold defined by Hinge Embedding Loss $margin$. Represented as $\mathrm{\Delta }$ in the formula. Default: 1.0.

• reduction (str) – Specify the computing method to be applied to the outputs: ‘none’, ‘mean’, or ‘sum’. Default: ‘mean’.

Inputs:

• logits (Tensor) - Tensor of shape $(\ast )$ where $\ast$ means any number of dimensions.

• labels (Tensor) - Same shape as the logits, contains -1 or 1.

Returns

Tensor or Tensor scalar, the computed loss depending on reduction.

Raises

• TypeError – If logits is not a Tensor.

• TypeError – If labels is not a Tensor.

• TypeError – If margin is not a float.

• ValueError – If labels does not have the same shape as logits.

• ValueError – If reduction is not one of ‘none’, ‘mean’, ‘sum’.

Supported Platforms:

Ascend GPU CPU

Examplse:

>>> import numpy as np
>>> from mindspore import Tensor
>>> import mindspore.nn as nn
>>> import mindspore.common.dtype as mstype
>>> arr1 = np.array([0.9, -1.2, 2, 0.8, 3.9, 2, 1, 0, -1]).reshape((3, 3))
>>> arr2 = np.array([1, 1, -1, 1, -1, 1, -1, 1, 1]).reshape((3, 3))
>>> logits = Tensor(arr1, mstype.float32)
>>> labels = Tensor(arr2, mstype.float32)
>>> loss = nn.HingeEmbeddingLoss(reduction='mean')
>>> output = loss(logits, labels)
>>> print(output)
0.16666667