文章目录

• 1. 自定义模型层
• 2. 使用预训练模型
• 3. 模型构建风格
• • 3.1 使用 `add_module` 方法
  • 3.2 添加进 `Sequential`
  • 3.3 Sequential作为模型容器
  • 3.4 ModuleList作为模型容器
  • 3.5 ModuleDict作为模型容器

当我们构建了数据管道能够将数据一个batch一个batch的取出来后，下一步就是构建模型了，模型的构建将很大程度的影响学习的效果，pytorch的模型层全部都在 torch.nn 模块下。

1. 自定义模型层

如果需要查看模型层的各个API及每个API的作用，大家可以去官网查看，网址放在这里了：https://pytorch.org/docs/stable/nn.html#shuffle-layers

如果需要自己定义模型层，那么需要继承 nn.Module 模块，类的初始化方法 __init__ 第一行必须调用父类的方法并定义模型层，必须实现 forward(input) 方法并将各层连接起来才行。

下面展示一个示例：

class Net(nn.Module):def __init__(self):super(Net, self).__init__()#调用父类的初始化方法# 3层lstmself.lstm = nn.LSTM(input_size = 3,hidden_size = 3,num_layers = 5,batch_first = True)self.linear = nn.Linear(3,3)self.block = Block()# 重写forward方法def forward(self,x_input):# 定义网络参数传递过程x = self.lstm(x_input)[0][:,-1,:]x = self.linear(x)y = self.block(x,x_input)return y

2. 使用预训练模型

因为迁移学习所带来的的影响，使用预训练的模型往往能够带来更好的效果，在pytorch中，很多预训练模型都集成到了 torchvision 中的 models 模块，可以在官网中查看支持的各个已训练好的模型，官网网址 https://pytorch.org/vision/stable/models.html，调用方法也十分简单，比如调用残差神经网络，只需要使用下面的语句即可

from torchvision import models
model = models.resnet152(pretrained=True)

上面的 pretrained=True 表示将模型下载下来，默认的下载路径为 C:\Users\ASUS\.cache\torch\hub\checkpoints ，下载的模型都存储在这里。

3. 模型构建风格

pytorch构建模型时有许多中风格可以用来添加网络层，第一种就是上面的那种继承 nn.Module 并且自定义类的风格，下面还有几种风格。

3.1 使用 add_module 方法

使用 add_module 能够王模型中添加模型层，示例如下

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))
net.add_module("sigmoid",nn.Sigmoid())print(net)

3.2 添加进 Sequential

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),nn.Sigmoid()
)print(net)

这种方式构建时不能给每个层指定名称。

3.3 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),nn.Sigmoid())def forward(self,x):x = self.conv(x)y = self.dense(x)return y 

3.4 ModuleList作为模型容器

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),nn.Sigmoid()])def forward(self,x):for layer in self.layers:x = layer(x)return x
net = Net()
print(net)

3.5 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),"sigmoid": nn.Sigmoid()})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)return x
net = Net()
print(net)