pytorch搭建神经网络经验总结

利用pytorch来构建网络模型有很多种方法，以下简单列出其中的四种:
假设构建一个网络模型如下：

卷积层–》Relu层–》池化层–》全连接层–》Relu层–》全连接层

首先导入几种方法用到的包：

import  torch
import  torch.nn.functional as F
from collection import OrderedDict

第一种方法

class Net1(torch.nn.Module):
    def __init__(self):
        super(Net1, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)
        self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)
        self.dense2 = torch.nn.Linear(128, 10)
 
    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv(x)), 2)
        x = x.view(x.size(0), -1)
        x = F.relu(self.dense1(x))
        x = self.dense2(x)
        return x
 
print("Method 1:")
model1 = Net1()
print(model1)

这种方法比较常用，早期的教程通常就是使用这种方法。

第二种方法

class Net2(torch.nn.Module):
    def __init__(self):
        super(Net2, self).__init__()
        self.conv = torch.nn.Sequential(
            torch.nn.Conv2d(3, 32, 3, 1, 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2))
        self.dense = torch.nn.Sequential(
            torch.nn.Linear(32 * 3 * 3, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 10)
        )
 
    def forward(self, x):
        conv_out = self.conv1(x)
        res = conv_out.view(conv_out.size(0), -1)
        out = self.dense(res)
        return out
 
print("Method 2:")
model2 = Net2()
print(model2)

这种方法利用**torch.nn.Sequential()**容器进行快速搭建，模型的各层被顺序添加到容器中。缺点是每层的编号是默认的阿拉伯数字，不易区分。

第三种方法

class Net3(torch.nn.Module):
    def __init__(self):
        super(Net3, self).__init__()
        self.conv = torch.nn.Sequential()
        self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))
        self.conv.add_module("relu1",torch.nn.ReLU())
        self.conv.add_module("pool1",torch.nn.MaxPool2d(2))
        self.dense = torch.nn.Sequential()
        self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))
        self.dense.add_module("relu2",torch.nn.ReLU())
        self.dense.add_module("dense2",torch.nn.Linear(128, 10))
 
    def forward(self, x):
        conv_out = self.conv1(x)
        res = conv_out.view(conv_out.size(0), -1)
        out = self.dense(res)
        return out
 
print("Method 3:")
model3 = Net3()
print(model3)

这种方法是对第二种方法的改进：通过add_module()添加每一层，并且为每一层增加了一个单独的名字。

完整代码

import torch
import torch.nn.functional as F
from collections import OrderedDict
 
# Method 1 -----------------------------------------
 
class Net1(torch.nn.Module):
    def __init__(self):
        super(Net1, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)
        self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)
        self.dense2 = torch.nn.Linear(128, 10)
 
    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv(x)), 2)
        x = x.view(x.size(0), -1)
        x = F.relu(self.dense1(x))
        x = self.dense2()
        return x
 
print("Method 1:")
model1 = Net1()
print(model1)
 
 
# Method 2 ------------------------------------------
class Net2(torch.nn.Module):
    def __init__(self):
        super(Net2, self).__init__()
        self.conv = torch.nn.Sequential(
            torch.nn.Conv2d(3, 32, 3, 1, 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2))
        self.dense = torch.nn.Sequential(
            torch.nn.Linear(32 * 3 * 3, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 10)
        )
 
    def forward(self, x):
        conv_out = self.conv1(x)
        res = conv_out.view(conv_out.size(0), -1)
        out = self.dense(res)
        return out
 
print("Method 2:")
model2 = Net2()
print(model2)
 
 
# Method 3 -------------------------------
class Net3(torch.nn.Module):
    def __init__(self):
        super(Net3, self).__init__()
        self.conv=torch.nn.Sequential()
        self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))
        self.conv.add_module("relu1",torch.nn.ReLU())
        self.conv.add_module("pool1",torch.nn.MaxPool2d(2))
        self.dense = torch.nn.Sequential()
        self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))
        self.dense.add_module("relu2",torch.nn.ReLU())
        self.dense.add_module("dense2",torch.nn.Linear(128, 10))
 
    def forward(self, x):
        conv_out = self.conv1(x)
        res = conv_out.view(conv_out.size(0), -1)
        out = self.dense(res)
        return out
 
print("Method 3:")
model3 = Net3()
print(model3)