mindspore.ops.KLDivLoss

class mindspore.ops.KLDivLoss(reduction='mean')

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

The updating formulas of KLDivLoss algorithm are as follows,

$$L=\{l_1,\dots ,l_N{\}}^{\mathrm{\top }},l_n=y_n\cdot (\log y_n-x_n)$$

Then,

$$\begin{array}{r}\ell (x,y)=\{\begin{array}{ll}L,& \text{if reduction}=\text{'none';}\\ \mathrm{mean}(L),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

where $x$ represents logits. $y$ represents labels. $\ell (x,y)$ represents output.

Parameters

reduction (str) – Specifies the reduction to be applied to the output. Its value must be one of ‘none’, ‘mean’, ‘sum’. Default: ‘mean’.

Inputs:

• logits (Tensor) - The input Tensor. The data type must be float32.

• 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 float32.

Supported Platforms:

GPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.kldiv_loss = ops.KLDivLoss()
...     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)
-0.23333333