SoftMarginLoss

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

Creates a criterion that measures a two-class soft margin loss between input predictions input and target labels label . It can be described as:

\[Out = log(1 + exp((-label * input)))\]
Parameters

  • 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 is 'mean'.

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

Shapes:

  • Input (Tensor): The input tensor with shape: [N, *], N is batch_size, * means any number of additional dimensions. The input ranges from -inf to inf Available dtype is float32, float64.

  • Label (Tensor): The target labels tensor with the same shape as input. The target labels which values should be numbers -1 or 1. Available dtype is int32, int64, float32, float64.

  • Output (Tensor): If reduction is 'none', the shape of output is same as input , else the shape of output is [].

Returns

A callable object of SoftMarginLoss.

Examples

>>> import paddle
>>> paddle.seed(2023)
>>> input = paddle.to_tensor([[0.5, 0.6, 0.7],[0.3, 0.5, 0.2]], 'float32')
>>> label = paddle.to_tensor([[1.0, -1.0, 1.0],[-1.0, 1.0, 1.0]], 'float32')
>>> soft_margin_loss = paddle.nn.SoftMarginLoss()
>>> output = soft_margin_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.64022040)

>>> input_np = paddle.uniform(shape=(5, 5), min=0.1, max=0.8, dtype="float64")
>>> label_np = paddle.randint(high=2, shape=(5, 5), dtype="int64")
>>> label_np[label_np==0]=-1
>>> input = paddle.to_tensor(input_np)
>>> label = paddle.to_tensor(label_np)
>>> soft_margin_loss = paddle.nn.SoftMarginLoss(reduction='none')
>>> output = soft_margin_loss(input, label)
>>> print(output)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1.10725628, 0.48778139, 0.56217249, 1.12581404, 0.51430043],
 [0.90375795, 0.37761249, 0.43007557, 0.95089798, 0.43288319],
 [1.16043599, 0.63015939, 0.51362715, 0.43617541, 0.57783301],
 [0.81927846, 0.52558369, 0.59713908, 0.83100696, 0.50811616],
 [0.82684205, 1.02064907, 0.50296995, 1.13461733, 0.93222519]])
forward ( input, label )

forward

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments