PaddlePaddle

 paddle.nn / functional / batch_norm


batch_norm

paddle.nn.functional.batch_norm(x, running_mean, running_var, weight, bias, training=False, momentum=0.9, epsilon=1e-05, data_format='NCHW', name=None):

推荐使用nn.BatchNorm1D,nn.BatchNorm2D, nn.BatchNorm3D,由内部调用此方法。

详情见 BatchNorm1D

参数:
  • x (int) - 输入,数据类型为float32, float64。

  • running_mean (Tensor) - 均值的Tensor。

  • running_var (Tensor) - 方差的Tensor。

  • weight (Tensor) - 权重的Tensor。

  • bias (Tensor) - 偏置的Tensor。

  • momentum (float, 可选) - 此值用于计算 moving_meanmoving_var 。默认值:0.9。更新公式如上所示。

  • epsilon (float, 可选) - 为了数值稳定加在分母上的值。默认值:1e-05。

  • data_format (string, 可选) - 指定输入数据格式,数据格式可以为“NC", "NCL", "NCHW" 或者"NCDHW"。默认值:"NCHW"。

  • name (string, 可选) – BatchNorm的名称, 默认值为None。更多信息请参见 Name

返回:无

代码示例

import paddle
import numpy as np

x = np.random.seed(123)
x = np.random.random(size=(2, 1, 2, 3)).astype('float32')
running_mean = np.random.random(size=1).astype('float32')
running_variance = np.random.random(size=1).astype('float32')
weight_data = np.random.random(size=1).astype('float32')
bias_data = np.random.random(size=1).astype('float32')
x = paddle.to_tensor(x)
rm = paddle.to_tensor(running_mean)
rv = paddle.to_tensor(running_variance)
w = paddle.to_tensor(weight_data)
b = paddle.to_tensor(bias_data)
batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv, w, b)
print(batch_norm_out)

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