一般的模型构建都是按照下图这样的流程

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下面分享一个自己手动搭建的网络

from model import *
import torchvision
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from torch import nn
from torch.utils.data import DataLoader#数据增强
data_transforms = transforms.Compose([transforms.RandomRotation(45),transforms.ToTensor(),])#准备数据集
#train_data = torchvision.datasets.CIFAR10(root="D:\pythonProject_pytorchstudy", train=True, transform=torchvision.transforms.ToTensor(), download=False)
#test_data = torchvision.datasets.CIFAR10(root="D:\pythonProject_pytorchstudy", train=False, transform=torchvision.transforms.ToTensor(), download=False)
train_data = torchvision.datasets.CIFAR10(root="D:\pythonProject_pytorchstudy", train=True, transform=data_transforms, download=False)
test_data = torchvision.datasets.CIFAR10(root="D:\pythonProject_pytorchstudy", train=False, transform=torchvision.transforms.ToTensor(), download=False)#数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为:{}".format(train_data_size))
print("测试集的长度为:{}".format(test_data_size))#利用Dataloader加载数据集
train_dataloader =DataLoader(train_data,batch_size=64)
test_dataloader =DataLoader(test_data,batch_size=64)#搭建神经网络
#model.py#创建网络模型
Yolo = My_Model()################################
if torch.cuda.is_available():  #Yolo = My_Model().cuda()   #
#################################损失函数
loss_fn = nn.CrossEntropyLoss()################################
if torch.cuda.is_available():  #loss_fn = loss_fn.cuda()   #
#################################优化器
learning_rate = 0.01 #1e-2 = 1 x (10)^(-2) =1/100 =0.01
optimizer  = torch.optim.SGD(Yolo.parameters(), lr = learning_rate, )#设置训练网络的参数
total_train_step = 0
#记录测试次数
total_test_step = 0
#训练轮数
epoch = 10#添加tensorboard
writer = SummaryWriter("D:\pythonProject_pytorchstudy\cifar-10-batches-py\logs_train")for i in range(epoch):print("第{}轮训练开始".format(i+1))#训练步骤开始Yolo.train()for data in train_dataloader:imgs,targets = data################################if torch.cuda.is_available():  #imgs = imgs.cuda()         #targets = targets.cuda()   #################################outputs = Yolo(imgs)loss  = loss_fn(outputs,targets)optimizer.zero_grad()loss.backward()optimizer.step()total_train_step += 1if total_train_step % 30 ==0:print("Iteration：{},loss:{}".format(total_train_step,loss.item()))writer.add_scalar("train_loss", loss.item(),total_train_step)#测试步骤开始Yolo.eval()total_test_loss = 0total_accuracy = 0with torch.no_grad(): #让网络中的梯度没有for data in test_dataloader:imgs, targets = data################################if torch.cuda.is_available():  #imgs = imgs.cuda()         #targets = targets.cuda()   #################################outputs = Yolo(imgs)loss = loss_fn(outputs,targets)total_test_loss = total_test_loss + loss.item()accuracy = (outputs.argmax(1) == targets).sum()total_accuracy = total_accuracy + accuracyprint("整体测试集上的Loss{}".format(total_test_loss))print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))writer.add_scalar("test_loss",total_test_loss,total_test_step)writer.add_scalar("test_accuracy",total_accuracy/test_data_size,total_test_step)total_train_step += 1torch.save(Yolo,"YOLO_{}".format(i+1))#torch.save(Yolo.state_dict(),"Yolo_{}.pth".format(i+1))print("模型已保存")writer.close()

import torch
from torch import nnclass My_Model(nn.Module):def __init__(self):super(My_Model, self).__init__()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),nn.Flatten(),nn.Linear(64 * 4 * 4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return x# Yolo = My_Model()# input = torch.ones(64,3,32,32)# output = Yolo(input)# print(output.shape)