import paddle
import numpy as np
import paddle.nn.functional as F

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 [N]
pred_loss = F.kl_div(paddle.to_tensor(input),
                        paddle.to_tensor(target), reduction='batchmean')
# shape=[5]

# 'mean' reduction, loss shape will be [1]
pred_loss = F.kl_div(paddle.to_tensor(input),
                        paddle.to_tensor(target), reduction='mean')
# shape=[1]

# 'sum' reduction, loss shape will be [1]
pred_loss = F.kl_div(paddle.to_tensor(input),
                        paddle.to_tensor(target), reduction='sum')
# shape=[1]

# 'none' reduction, loss shape is same with input shape
pred_loss = F.kl_div(paddle.to_tensor(input),
                        paddle.to_tensor(target), reduction='none')
# shape=[5, 20]