mindspore.nn.HingeEmbeddingLoss

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

Calculate the Hinge Embedding Loss value based on the input 'logits' and' labels' (only including 1 or -1). Usually used to measure 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, int) – Threshold defined by Hinge Embedding Loss $margin$. Represented as $\mathrm{\Delta }$ in the formula. Default: 1.0 .

• reduction (str, optional) –

  Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'mean' .

  • 'none': no reduction will be applied.

  • 'mean': compute and return the mean of elements in the output.

  • 'sum': the output elements will be summed.

Inputs:

• logits (Tensor) - The predicted value, expressed as $x$ in the equation. Tensor of shape $(\ast )$ where $\ast$ means any number of dimensions.

• labels (Tensor) - Label value, represented as $y$ in the equation. 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 or int.

• ValueError – If labels does not have the same shape as logits or they could not broadcast to each other.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> import numpy as np
>>> 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 = ms.Tensor(arr1, ms.float32)
>>> labels = ms.Tensor(arr2, ms.float32)
>>> loss = nn.HingeEmbeddingLoss(reduction='mean')
>>> output = loss(logits, labels)
>>> print(output)
0.16666667