卷积神经网络-训练代码（cifar10）

import torch.optim.sgd
import torchvision
# 准备数据集
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
# 定义训练的设备
device=torch.device('cpu')



writer=SummaryWriter('./log_train')

train_data=torchvision.datasets.CIFAR10(root='./data',train=True,transform=torchvision.transforms.ToTensor(),
                                        download=True)
test_data=torchvision.datasets.CIFAR10(root='./data',train=False,transform=torchvision.transforms.ToTensor(),
                                       download=True)

train_data_size=len(train_data)
test_data_size=len(test_data)
# 格式化字符串
print('训练数据集长度{}'.format(train_data_size))
print('测试数据集长度{}'.format(test_data_size))
# 利用dataloader来加载数据集
train_dataloader=DataLoader(train_data,batch_size=64)
test_dataloader=DataLoader(test_data,batch_size=64)
# 搭建神经网络
class li(nn.Module):
    def __init__(self):
        super(li, 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

LI=li()

LI=LI.to(device)

# 创建损失函数
loss_fn=nn.CrossEntropyLoss()
loss_fn=loss_fn.to(device)

# 优化器
learning_rate=0.01
optimizer=torch.optim.SGD(LI.parameters(),lr=learning_rate)

# 设置训练网络的一些参数
# 记录训练次数
total_train_step=0
# 记录测试次数
total_test_step=0
# 训练的轮数
epoch=20

for i in range(epoch):
    print('---------第{}轮开始'.format(i+1))
    # 训练开始

    LI.train()
    for data in train_dataloader:
        imgs,targets=data
        imgs=imgs.to(device)
        targets=targets.to(device)
        outputs=LI(imgs)
        loss=loss_fn(outputs,targets)

        optimizer.zero_grad()
        loss.backward()#梯度下降计算新的梯度
        optimizer.step()

        total_train_step=total_train_step+1

        if total_train_step%100==0:

            print('训练次数{}，loss{}'.format(total_train_step,loss))
            writer.add_scalar('train_loss',loss.item(),total_train_step)
# 测试步骤
    LI.eval()  #调用模块
    total_test_loss=0
    total_accuracy=0
    with torch.no_grad():
        for data in test_dataloader:
            imgs,targets=data
            imgs = imgs.to(device)
            targets = targets.to(device)
            outputs=LI(imgs)
            loss=loss_fn(outputs,targets)
            total_test_loss=total_test_loss+loss.item()
            accuracy=(outputs.argmax(1)==targets).sum()
            total_accuracy+=accuracy
    print('整体loss{}'.format(total_test_loss))
    print('zhengquelv-整体:{}'.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_test_step+=1

    torch.save(LI,'li{}.pth'.format(i))
    print('saved')
writer.close()

标签：loss,nn,cifar10,卷积,神经网络,train,test,total,data
来源： https://blog.csdn.net/Li12139/article/details/122293879