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。

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_mean 和 moving_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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paddle.nn / functional / batch_norm

batch_norm¶

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paddle.nn / functional / batch_norm

batch_norm¶