Python_pytorch(四)网络搭建

搭建架构

import torch
import torchvision
from torch import nn
from torch.nn import Flatten
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("./data",train=True,transform=torchvision.transforms.ToTensor(),download=True)
data_load=DataLoader(dataset,batch_size=64,drop_last=True)
class My_mod(nn.Module):
    def __init__(self):
        super(My_mod,self).__init__()
        self.conv1=nn.Conv2d(3,32,5,1,2)
        self.maxpool=nn.MaxPool2d(2)
        self.conv2=nn.Conv2d(32,32,5,1,2)
        self.conv3=nn.Conv2d(32,64,5,1,2)
        self.relu=nn.ReLU()
        self.flatten=Flatten()
        self.linear=nn.Linear(1024,10)
        self.model=nn.Sequential(
            nn.Conv2d(3, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.MaxPool2d(2),
            Flatten(),
            nn.Linear(1024, 10)

        )
    def forward(self,x):
        x=self.model(x)
        return x

my_mod=My_mod()

for data in data_load:
    img,tar=data
    output=my_mod(img)
    print(output.shape)

writer=SummaryWriter("./learb_log")
img=torch.randn((64,3,32,32))
writer.add_graph(my_mod,img)
writer.close()

TensorBoard 显示网络架构