batchnorm代码
时间: 2023-09-12 11:03:34 浏览: 37
以下是一个简单的批归一化代码实现:
```python
import numpy as np
class BatchNorm:
def __init__(self, input_shape, momentum=0.99, eps=1e-5):
self.momentum = momentum
self.eps = eps
self.gamma = np.ones(input_shape)
self.beta = np.zeros(input_shape)
self.running_mean = np.zeros(input_shape)
self.running_var = np.zeros(input_shape)
def forward(self, x, train=True):
if train:
batch_mean = np.mean(x, axis=0)
batch_var = np.var(x, axis=0)
self.running_mean = self.momentum * self.running_mean + (1 - self.momentum) * batch_mean
self.running_var = self.momentum * self.running_var + (1 - self.momentum) * batch_var
else:
batch_mean = self.running_mean
batch_var = self.running_var
x_norm = (x - batch_mean) / np.sqrt(batch_var + self.eps)
out = self.gamma * x_norm + self.beta
return out, x_norm
def backward(self, dout, x, x_norm):
m = x.shape[0]
dx_norm = dout * self.gamma
dvar = np.sum(dx_norm * (x - self.running_mean) * (-0.5) * np.power(self.running_var + self.eps, -1.5), axis=0)
dmean = np.sum(dx_norm * (-1) / np.sqrt(self.running_var + self.eps), axis=0) + dvar * np.mean(-2 * (x - self.running_mean), axis=0)
dx = dx_norm / np.sqrt(self.running_var + self.eps) + dvar * 2 * (x - self.running_mean) / m + dmean / m
dgamma = np.sum(dout * x_norm, axis=0)
dbeta = np.sum(dout, axis=0)
return dx, dgamma, dbeta
```
该代码实现了一个简单的批归一化类 `BatchNorm`,包括前向传播和反向传播的实现。在前向传播中,根据输入数据和训练标志,计算出批次均值和方差,并更新 `running_mean` 和 `running_var`,然后计算出归一化数据并乘以缩放因子 `gamma` 并加上偏移量 `beta`。在反向传播中,首先计算出 `dout` 对 `gamma` 和 `beta` 的导数,然后计算出 `dx_norm`, `dvar` 和 `dmean`,最后根据链式法则计算出 `dx`, `dgamma` 和 `dbeta`。