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!6379 SmoothL1Loss parameter name change and doc update 

Merge pull request !6379 from Peilin/nn-smoothL1Loss-sigmabeta-rename
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mindspore-ci-bot 2020-09-18 00:19:40 +08:00 committed by Gitee
parent a32b6f8abf c0065a6930
commit 1256737a7c
1 changed files with 7 additions and 7 deletions
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14
mindspore/nn/loss/loss.py
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@ -158,16 +158,16 @@ class SmoothL1Loss(_Loss):
.. math::
L_{i} =
\begin{cases}
0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < \text{sigma}; \\
|x_i - y_i| - 0.5, & \text{otherwise. }
\frac{0.5 (x_i - y_i)^{2}}{\text{beta}}, & \text{if } |x_i - y_i| < \text{beta} \\
|x_i - y_i| - 0.5 \text{beta}, & \text{otherwise. }
\end{cases}
Here :math:`\text{sigma}` controls the point where the loss function changes from quadratic to linear.
Here :math:`\text{beta}` controls the point where the loss function changes from quadratic to linear.
Its default value is 1.0. :math:`N` is the batch size. This function returns an
unreduced loss Tensor.
Args:
sigma (float): A parameter used to control the point where the function will change from
beta (float): A parameter used to control the point where the function will change from
quadratic to linear. Default: 1.0.
Inputs:
@ -183,10 +183,10 @@ class SmoothL1Loss(_Loss):
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> loss(input_data, target_data)
"""
def __init__(self, sigma=1.0):
def __init__(self, beta=1.0):
super(SmoothL1Loss, self).__init__()
self.sigma = sigma
self.smooth_l1_loss = P.SmoothL1Loss(self.sigma)
self.beta = beta
self.smooth_l1_loss = P.SmoothL1Loss(self.beta)
def construct(self, base, target):
return self.smooth_l1_loss(base, target)