KLDivLoss¶

class paddle.nn. KLDivLoss ( reduction='mean' ) [source]

Generate a callable object of ‘KLDivLoss’ to calculate 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:

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

Parameters

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'.

Shape:

• input (Tensor): (N, *), where * means, any number of additional dimensions.

• label (Tensor): (N, *), same shape as input.

• output (Tensor): tensor with shape: [1] by default.

Examples

import paddle
import paddle.nn as nn

shape = (5, 20)
x = paddle.uniform(shape, min=-10, max=10).astype('float32')
target = paddle.uniform(shape, min=-10, max=10).astype('float32')

# 'batchmean' reduction, loss shape will be [1]
kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
pred_loss = kldiv_criterion(x, target)
# shape=[1]

# 'mean' reduction, loss shape will be [1]
kldiv_criterion = nn.KLDivLoss(reduction='mean')
pred_loss = kldiv_criterion(x, target)
# shape=[1]

# 'sum' reduction, loss shape will be [1]
kldiv_criterion = nn.KLDivLoss(reduction='sum')
pred_loss = kldiv_criterion(x, target)
# shape=[1]

# 'none' reduction, loss shape is same with X shape
kldiv_criterion = nn.KLDivLoss(reduction='none')
pred_loss = kldiv_criterion(x, target)
# shape=[5, 20]

forward ( input, label )

forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments