# HingeEmbeddingLoss¶

class paddle.nn. HingeEmbeddingLoss ( margin=1.0, reduction='mean', name=None ) [source]

Create a callable object of HingeEmbeddingLoss to calculates hinge_embedding_loss. Measures the loss given an input tensor $$x$$ and a labels tensor $$y$$, and is typically used for learning nonlinear embeddings or semi-supervised learning.

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

$\begin{split}l_n = \begin{cases} x_n, & \text{if}\; y_n = 1,\\ \max \{0, \Delta - x_n\}, & \text{if}\; y_n = -1, \end{cases}\end{split}$

and the total loss functions is

$\begin{split}\ell(x, y) = \begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}$

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

Parameters
• margin (float, optional) – Specifies the hyperparameter margin to be used. The value determines how large the input need to be to calculate in hinge_embedding_loss. When label is -1, Input smaller than margin are minimized with hinge_embedding_loss. Default = 1.0

• reduction (str, optional) – Indicate how to average the loss by batch_size, the candidates are 'none' | 'mean' | 'sum'. If reduction is 'none', the unreduced loss is returned; If reduction is 'mean', the reduced mean loss is returned; If reduction is 'sum', the summed loss is returned. Default: 'mean'

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Call Parameters:

input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,…, Dk), k >= 1.

label (Tensor): Label tensor containing 1 or -1, the data type is float32 or float64. The shape of label is the same as the shape of input.

Shape:

input: N-D Tensor, the shape is [N, *], N is batch size and * means any number of additional dimensions, available dtype is float32, float64. The sum operationoperates over all the elements.

label: N-D Tensor, same shape as the input.

output: scalar. If reduction is 'none', then same shape as the input.

Returns

Tensor, The tensor variable storing the hinge_embedding_loss of input and label.

Examples

>>> import paddle

>>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
>>> # label elements in {1., -1.}
>>> label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)

>>> hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='none')
>>> loss = hinge_embedding_loss(input, label)
>>> print(loss)
[[ 0., -2.,  0.],
[ 0., -1.,  2.],
[ 1.,  1.,  1.]])

>>> hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')
>>> loss = hinge_embedding_loss(input, label)
>>> print(loss)