# kl_div¶

paddle.nn.functional. kl_div ( input, label, reduction='mean', name=None ) [source]

This operator calculates the Kullback-Leibler divergence loss between Input(X) and Input(Target). Notes that Input(X) is the log-probability and Input(Target) is the probability.

KL divergence loss is calculated as follows:

\$\$l(x, y) = y * (log(y) - x)\$\$

While \(x\) is input and \(y\) is label.

While `reduction` is `none`, output loss is in the same shape as input, loss in each point is calculated seperately and no reduction is applied.

While `reduction` is `mean`, output loss is in shape of  and loss value is the mean value of all losses.

While `reduction` is `sum`, output loss is in shape of  and loss value is the sum value of all losses.

While `reduction` is `batchmean`, output loss is in shape of  and loss value is the sum value of all losses divided by batch size.

Parameters
• input (Tensor) – The input tensor. The shapes is [N, *], where N is batch size and * means any number of additional dimensions. It’s data type should be float32, float64.

• label (Tensor) – label. The shapes is [N, *], same shape as `input` . It’s data type should be float32, float64.

• reduction (Tensor) – Indicate how to average the loss, the candicates are `'none'` | `'batchmean'` | `'mean'` | `'sum'`. If reduction is `'mean'`, the reduced mean loss is returned; If reduction is `'batchmean'`, the sum loss divided by batch size is returned; if reduction is `'sum'`, the reduced sum loss is returned; if reduction is `'none'`, no reduction will be apllied. Default is `'mean'`.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

The KL divergence loss. The data type is same as input tensor

Return type

Tensor

Examples

```import paddle
import numpy as np

shape = (5, 20)
input = np.random.uniform(-10, 10, shape).astype('float32')
target = np.random.uniform(-10, 10, shape).astype('float32')

# 'batchmean' reduction, loss shape will be 
# shape=

# 'mean' reduction, loss shape will be