mindspore.ops.SmoothL1Loss

class mindspore.ops.SmoothL1Loss(beta=1.0, reduction='none')

Calculate the smooth L1 loss, and the L1 loss function has robustness.

Refer to mindspore.ops.smooth_l1_loss() for more details.

Parameters

• beta (number, optional) –

  A parameter used to control the point where the function will change between L1 to L2 loss. Default: 1.0 .

  • Ascend: The value should be equal to or greater than zero.

  • CPU/GPU: The value should be greater than zero.

• reduction (str, optional) –

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

  • '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) - Input Tensor of any dimension. Supported dtypes:

  • Ascend: float16, float32, bfloat16.

  • CPU/GPU: float16, float32, float64.

• labels (Tensor) - Ground truth data.

  • CPU/Ascend: has the same shape as the logits, logits and labels comply with the implicit type conversion rules to make the data types consistent.

  • GPU: has the same shape and dtype as the logits.

Outputs:

Tensor, if reduction is 'none', then output is a tensor with the same shape as logits. Otherwise the shape of output tensor is \(()\).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> loss = ops.SmoothL1Loss()
>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
[0.  0.  0.5]