mindspore.ops.KLDivLoss
- class mindspore.ops.KLDivLoss(reduction='mean')[source]
Computes the Kullback-Leibler divergence between the logits and the labels.
The updating formulas of KLDivLoss algorithm are as follows,
\[L = \{l_1,\dots,l_N\}^\top, \quad l_n = y_n \cdot (\log y_n - x_n)\]Then,
\[\begin{split}\ell(x, y) = \begin{cases} L, & \text{if reduction} = \text{'none';}\\ \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]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’ or ‘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
- 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