cifar10 model结构实现

cifar10 model structure

 代码示例：

import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d,MaxPool2d,Flatten,Linear,Sequential
from torch.utils.tensorboard import SummaryWriter

class myModel(nn.Module):
    def __init__(self):
        super(myModel,self).__init__()
        # self.conv1=Conv2d(in_channels=3,out_channels=32,kernel_size=5,stride=1,padding=2)
        # self.maxpool1=MaxPool2d(kernel_size=2)
        # self.conv2=Conv2d(in_channels=32,out_channels=32,kernel_size=5,stride=1,padding=2)
        # self.maxpool2=MaxPool2d(kernel_size=2)
        # self.conv3=Conv2d(in_channels=32,out_channels=64,kernel_size=5,stride=1,padding=2)
        # self.maxpool3=MaxPool2d(kernel_size=2)
        # self.flattern=Flatten()
        # self.linear1=Linear(1024,64)
        # self.linear2=Linear(64,10)
        self.model=Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self,x):
        # x=self.conv1(x)
        # x=self.maxpool1(x)
        # x=self.conv2(x)
        # x=self.maxpool2(x)
        # x=self.conv3(x)
        # x=self.maxpool3(x)
        # x=self.flattern(x)
        # x=self.linear1(x)
        # x=self.linear2(x)
        x=self.model(x)
        return x

mymodel=myModel()
print(mymodel)
input=torch.ones((64,3,32,32))
print(input)
output=mymodel(input)
print(output.shape)

writer=SummaryWriter('seq_logs')
writer.add_graph(mymodel,input)
writer.close()

其中根据下图的公式计算padding数

 另外，Sequential把网络每一层按照顺序进行编号，在forward里面按照序号执行。