cifar10 模型的训练和测试

一、模型结构

import torch
from torch import nn


class  Cifar10Model(nn.Module):
    def __init__(self):
        super(Cifar10Model, 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

if __name__=='__main__':
    tudui=Cifar10Model()
    input=torch.ones(64,3,32,32)
    output=tudui(input)
    print(output.shape)

训练和测试：

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

from Model.Cifar10Model import Cifar10Model

train_data= torchvision.datasets.CIFAR10(root="../dataset",train=True,transform=torchvision.transforms.ToTensor(),download=True)

test_data= torchvision.datasets.CIFAR10(root="../dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)


train_data_size=len(train_data)
test_data_size=len(test_data)
print("训练数据集的长度是：{}".format(train_data_size))
print("测试数据集的长度是：{}".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)

#创建网络模型
cifar10Model=Cifar10Model()

#损失函数
loss_fn=nn.CrossEntropyLoss()
#优化器
#1e-2=1*(10)^(-2)=1/100=0.01
learning_rate=1e-2
optimzer=torch.optim.SGD(cifar10Model.parameters(),lr=learning_rate)

#设置训练网络的一些参数
total_train_step=0
total_test_step=0

epoch=10

#添加 tensorboard
write= SummaryWriter("../logs_train")

for i in range(epoch):
    print("----第{}轮训练开始-----".format(i+1))
    #训练步骤开始
    for data in train_dataloader:
        imgs,targets=data
        outputs=cifar10Model(imgs)
        loss =loss_fn(outputs,targets)
        #优化器优化模型
        optimzer.zero_grad()
        loss.backward()
        optimzer.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.item()))
            write.add_scalar("train_loss",loss.item(),total_train_step)

     #测试步骤开始
    total_test_loss=0
    total_accuracy=0
    with torch.no_grad():
        for data in test_dataloader:
            ims,targets=data
            output=cifar10Model(ims)
            loss=loss_fn(output,targets)
            total_test_loss=total_test_loss+loss.item()
            accuray=(output.argmax(1)==targets.sum())
            total_accuracy=total_accuracy+accuray

    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))
    write.add_scalar("tess_loss",total_test_loss,total_test_step)
    write.add_scalar("test_accuracy",total_accuracy/test_data_size,total_test_step)
    total_test_step=total_test_step+1

    torch.save(cifar10Model,"cifar10Model_{}.path".format(i))
    print("模型已保存")

    write.close()