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

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(gpu:0), 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(gpu:0), stop_gradient=True,
#        [[0.61739663, 0.51405668, 1.09346100, 0.42385561, 0.91602303],
#         [0.76997038, 1.01977148, 0.98971722, 1.13976032, 0.88152088],
#         [0.55476735, 1.10505384, 0.89923519, 0.45018155, 1.06587511],
#         [0.37998142, 0.48067240, 0.47791212, 0.55664053, 0.98581399],
#         [0.78571653, 0.59319711, 0.39701841, 0.76172109, 0.83781742]])
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