如何在 PyTorch 中做完全连接的批量归一化？

Question

torch.nn 有 classes BatchNorm1d、BatchNorm2d、BatchNorm3d，但它没有完全连接的 BatchNorm class ？在 PyTorch 中执行正常 Batch Norm 的标准方法是什么？

Answer 1

好的。我想到了。 BatchNorm1d 也可以处理 Rank-2 张量，因此可以将 BatchNorm1d 用于正常的全连接情况。

例如：

import torch.nn as nn


class Policy(nn.Module):
def __init__(self, num_inputs, action_space, hidden_size1=256, hidden_size2=128):
    super(Policy, self).__init__()
    self.action_space = action_space
    num_outputs = action_space

    self.linear1 = nn.Linear(num_inputs, hidden_size1)
    self.linear2 = nn.Linear(hidden_size1, hidden_size2)
    self.linear3 = nn.Linear(hidden_size2, num_outputs)
    self.bn1 = nn.BatchNorm1d(hidden_size1)
    self.bn2 = nn.BatchNorm1d(hidden_size2)

def forward(self, inputs):
    x = inputs
    x = self.bn1(F.relu(self.linear1(x)))
    x = self.bn2(F.relu(self.linear2(x)))
    out = self.linear3(x)


    return out

Answer 2

BatchNorm1d 通常在 ReLU 之前，偏差是多余的，所以

import torch.nn as nn

class Policy(nn.Module):
def __init__(self, num_inputs, action_space, hidden_size1=256, hidden_size2=128):
    super(Policy2, self).__init__()
    self.action_space = action_space
    num_outputs = action_space

    self.linear1 = nn.Linear(num_inputs, hidden_size1, bias=False)
    self.linear2 = nn.Linear(hidden_size1, hidden_size2, bias=False)
    self.linear3 = nn.Linear(hidden_size2, num_outputs)
    self.bn1 = nn.BatchNorm1d(hidden_size1)
    self.bn2 = nn.BatchNorm1d(hidden_size2)

def forward(self, inputs):
    x = inputs
    x = F.relu(self.bn1(self.linear1(x)))
    x = F.relu(self.bn2(self.linear2(x)))
    out = self.linear3(x)

    return out

如何在 PyTorch 中做完全连接的批量归一化？

How to do fully connected batch norm in PyTorch?

python

neural-network

deep-learning

pytorch

batch-normalization