pytorch建模的三种方式

# 可以使用以下3种方式构建模型：
#
# 1，继承nn.Module基类构建自定义模型。
#
# 2，使用nn.Sequential按层顺序构建模型。
#
# 3，继承nn.Module基类构建模型并辅助应用模型容器进行封装(nn.Sequential,nn.ModuleList,nn.ModuleDict)。
#
# 其中 第1种方式最为常见，第2种方式最简单，第3种方式最为灵活也较为复杂。

# 一、继承nn.Module基类构建自定义模型

from torch import nn
class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)self.pool1 = nn.MaxPool2d(kernel_size = 2,stride = 2)self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)self.pool2 = nn.MaxPool2d(kernel_size = 2,stride = 2)self.dropout = nn.Dropout2d(p = 0.1)self.adaptive_pool = nn.AdaptiveMaxPool2d((1,1))self.flatten = nn.Flatten()self.linear1 = nn.Linear(64,32)self.relu = nn.ReLU()self.linear2 = nn.Linear(32,1)def forward(self,x):x = self.conv1(x)x = self.pool1(x)x = self.conv2(x)x = self.pool2(x)x = self.dropout(x)x = self.adaptive_pool(x)x = self.flatten(x)x = self.linear1(x)x = self.relu(x)y = self.linear2(x)return y
net = Net()
print(net)
#查看参数
from torchkeras import summary
summary(net,input_shape= (3,32,32));

 # 二、使用nn.Sequential按层顺序构建模型 # 利用add_module方法

net = nn.Sequential()
net.add_module("conv1",nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3))
net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))
net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("dropout",nn.Dropout2d(p = 0.1))
net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
net.add_module("flatten",nn.Flatten())
net.add_module("linear1",nn.Linear(64,32))
net.add_module("relu",nn.ReLU())
net.add_module("linear2",nn.Linear(32,1))
print(net)

# 利用变长参数

net = nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1)
)
print(net)

# 三、继承nn.Module基类构建模型并辅助应用模型容器进行封装
# nn.Sequential作为模型容器

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv = nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)))self.dense = nn.Sequential(nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1))def forward(self,x):x = self.conv(x)y = self.dense(x)return y
net = Net()
print(net)

# nn.ModuleList作为模型容器
# 注意下面中的ModuleList不能用Python中的列表代替。（即不用省略）

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.layers = nn.ModuleList([nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1)])def forward(self,x):for layer in self.layers:x = layer(x)return x
net = Net()
print(net)

# nn.ModuleDict作为模型容器

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.layers_dict = nn.ModuleDict({"conv1":nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),"pool": nn.MaxPool2d(kernel_size = 2,stride = 2),"conv2":nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),"dropout": nn.Dropout2d(p = 0.1),"adaptive":nn.AdaptiveMaxPool2d((1,1)),"flatten": nn.Flatten(),"linear1": nn.Linear(64,32),"relu":nn.ReLU(),"linear2": nn.Linear(32,1)})def forward(self,x):layers = ["conv1","pool","conv2","pool","dropout","adaptive","flatten","linear1","relu","linear2","sigmoid"]for layer in layers:x = self.layers_dict[layer](x) # 只找有的 sigmoid是没有的return x
net = Net()
print(net)