batch+normalization
时间: 2023-12-03 08:05:39 浏览: 69
Batch Normalization是一种用于加速深度神经网络训练的技术。它通过在每个小批量数据上标准化网络的输入来解决内部协变量偏移问题。Batch Normalization的步骤如下:
1. 对于每个小批量数据,计算其均值和方差。
2. 使用计算出的均值和方差对小批量数据进行标准化。
3. 对标准化后的数据进行缩放和平移,得到最终的输出。
4. 为了增加网络的表达能力,引入可学习的缩放和平移参数。
下面是一个使用Batch Normalization的例子:
```python
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.bn1 = nn.BatchNorm2d(6)
self.conv2 = nn.Conv2d(6, 16, 5)
self.bn2 = nn.BatchNorm2d(16)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.bn3 = nn.BatchNorm1d(120)
self.fc2 = nn.Linear(120, 84)
self.bn4 = nn.BatchNorm1d(84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, (2, 2))
x = self.conv2(x)
x = self.bn2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, (2, 2))
x = x.view(-1, self.num_flat_features(x))
x = self.fc1(x)
x = self.bn3(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = self.bn4(x)
x = nn.functional.relu(x)
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
```