一般的模型构建都是按照下图这样的流程
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 += 1 if 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 = 0 total_accuracy = 0 with 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 + accuracy print("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 += 1 torch.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 nn class 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)
