【土堆 pytorch实战】P22神经网络搭建

P22搭建CIFAR-10

卷积层1：输入H=32，kernel_size=5,dilation=1 输出H=32，代入公式得 $32=\frac{32+2\ast padding-4-1}{stride}+1$，所以当stride=1时，padding=2

from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui,self).__init__()
        self.conv1=Conv2d(3,32,5,padding=2)
        self.maxpool1=MaxPool2d(2)  #池化层默认的stride=kernel_size
        self.conv2=Conv2d(32,32,5,1,padding=2)
        self.maxpool2=MaxPool2d(2)
        self.conv3=Conv2d(32,64,5,padding=2)
        self.maxpool3=MaxPool2d(2)
        self.flatten=Flatten()
        self.linear1=Linear(1024,64)
        self.linear2=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.flatten(x)
        x=self.linear1(x)
        x=self.linear2(x)
        return x
tudui=Tudui()
print(tudui)

• 使用Sequential 搭建网络

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui,self).__init__()
        self.model1=Sequential(
            Conv2d(3,32,5,padding=2),
            MaxPool2d(2),
            Conv2d(32,32,5,1,padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self,x):
        x=self.model1(x)
        return x
tudui=Tudui()
print(tudui)
input=torch.ones((64,3,32,32))
output=tudui(input)
print(output.shape)

• 使用tensorboard显示

writer=SummaryWriter("logs_seq")
writer.add_graph(tudui,input)
writer.close()