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 as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np第一种方法
print("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.conv1(x)), 2)
x = x.view(x.size(0), -1)
x = F.relu(self.dense1(x))
x = self.dense2(x)
return x
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
print("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
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：
print("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
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。
第四种方法：
print("Method 4 -----------------------------------------")
class Net4(torch.nn.Module):
def __init__(self):
super(Net4, self).__init__()
self.conv = torch.nn.Sequential(
OrderedDict(
[
("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),
("relu1", torch.nn.ReLU()),
("pool", torch.nn.MaxPool2d(2))
]
))
self.dense = torch.nn.Sequential(
OrderedDict([
("dense1", torch.nn.Linear(32 * 3 * 3, 128)),
("relu2", torch.nn.ReLU()),
("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
model4 = Net4()
print(model4)
是第三种方法的另外一种写法，通过字典的形式添加每一层，并且设置单独的层名称。
完整代码：
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
print("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(x)
return x
model1 = Net1()
print(model1)
print("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
model2 = Net2()
print(model2)
print("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
model3 = Net3()
print(model3)
print("Method 4 -----------------------------------------")
class Net4(torch.nn.Module):
def __init__(self):
super(Net4, self).__init__()
self.conv = torch.nn.Sequential(
OrderedDict(
[
("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),
("relu1", torch.nn.ReLU()),
("pool", torch.nn.MaxPool2d(2))
]
))
self.dense = torch.nn.Sequential(
OrderedDict([
("dense1", torch.nn.Linear(32 * 3 * 3, 128)),
("relu2", torch.nn.ReLU()),
("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
model4 = Net4()
print(model4)最后输出
Method 1 -----------------------------------------
Net1(
(conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(dense1): Linear(in_features=288, out_features=128, bias=True)
(dense2): Linear(in_features=128, out_features=10, bias=True)
)
Method 2 -----------------------------------------
Net2(
(conv): Sequential(
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(dense): Sequential(
(0): Linear(in_features=288, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=10, bias=True)
)
)
Method 3 -----------------------------------------
Net3(
(conv): Sequential(
(conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu1): ReLU()
(pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(dense): Sequential(
(dense1): Linear(in_features=288, out_features=128, bias=True)
(relu2): ReLU()
(dense2): Linear(in_features=128, out_features=10, bias=True)
)
)
Method 4 -----------------------------------------
Net4(
(conv): Sequential(
(conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu1): ReLU()
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(dense): Sequential(
(dense1): Linear(in_features=288, out_features=128, bias=True)
(relu2): ReLU()
(dense2): Linear(in_features=128, out_features=10, bias=True)
)
)