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mindspore.ops.KLDivLoss

class mindspore.ops.KLDivLoss(reduction='mean')[source]

Computes the Kullback-Leibler divergence between the logits and the labels.

For tensors of the same shape $x$ and $t a r g e t$ , the updating formulas of KLDivLoss algorithm are as follows,

L (x, t a r g e t) = t a r g e t \cdot (\log t a r g e t - x)

Then,

\begin{array}{r} ℓ (x, t a r g e t) = {\begin{cases} L (x, t a r g e t), & if reduction ='none'; \\ mean (L (x, t a r g e t)), & if reduction ='mean'; \\ sum (L (x, t a r g e t)) / x . shape [0], & if reduction ='batchmean'; \\ sum (L (x, t a r g e t)), & if reduction ='sum'. \end{cases} \end{array}

where $x$ represents logits. $t a r g e t$ represents labels. $ℓ (x, t a r g e t)$ represents output.

Note

On Ascend, float64 dtype is not currently supported. The output aligns with the mathematical definition of KL divergence only when reduction is set to ‘batchmean’.

Parameters

reduction (str) –

Specifies the reduction to be applied to the output. Default: ‘mean’.

On Ascend, the value of reduction must be one of ‘batchmean’, ‘none’ or ‘sum’.
On GPU, the value of reduction must be one of ‘mean’, ‘none’ or ‘sum’.
On CPU, the value of reduction must be one of ‘mean’, ‘batchmean’, ‘none’ or ‘sum’.

Inputs:

logits (Tensor) - The input Tensor. The data type must be float16, float32 or float64.
labels (Tensor) - The label Tensor which has the same shape and data type as logits.

Outputs:

Tensor or Scalar, if reduction is ‘none’, then output is a tensor and has the same shape as logits. Otherwise it is a scalar.

Raises

TypeError – If reduction is not a str.
TypeError – If neither logits nor labels is a Tensor.
TypeError – If dtype of logits or labels is not currently supported.
ValueError – If shape of logits is not the same as labels.
RuntimeError – If logits or labels is a scalar when reduction is ‘batchmean’.

Supported Platforms:: Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.kldiv_loss = ops.KLDivLoss(reduction='sum')
...     def construct(self, logits, labels):
...         result = self.kldiv_loss(logits, labels)
...         return result
...
>>> net = Net()
>>> logits = Tensor(np.array([0.2, 0.7, 0.1]), mindspore.float32)
>>> labels = Tensor(np.array([0., 1., 0.]), mindspore.float32)
>>> output = net(logits, labels)
>>> print(output)
-0.7