soft_margin_loss

paddle.nn.functional. soft_margin_loss ( input, label, reduction='mean', name=None ) [source]

The API measures the soft margin loss between input predictions input and target labels label . It can be described as:

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

  • input (Tensor) – The input predications 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.

  • 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.

Returns

If reduction is 'none', the shape of output is same as input , else the shape of output is [].

Return type

Output (Tensor)

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')
output = paddle.nn.functional.soft_margin_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        0.64022040)

input = paddle.uniform(shape=(5, 5), dtype="float32", min=0.1, max=0.8)
label = paddle.randint(0, 2, shape=(5, 5), dtype="int64")
label[label==0]=-1

output = paddle.nn.functional.soft_margin_loss(input, label, reduction='none')
print(output)
# Tensor(shape=[5, 5], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [[1.09917796, 0.52613139, 0.56263304, 0.82736146, 0.38776723],
#         [1.07179427, 1.11924267, 0.49877715, 1.10026348, 0.46184641],
#         [0.84367639, 0.74795729, 0.44629076, 0.55123353, 0.77659678],
#         [0.39465919, 0.76651484, 0.54485321, 0.76609844, 0.77166790],
#         [0.51283568, 0.84757161, 0.78913331, 1.05268764, 0.45318675]])