pytorch(四）模型训练方法

训练步骤01

import torchvision
from torch.utils.data import DataLoader
from torch import nn
from model import *
#训练数据集
train_data = torchvision.datasets.CIFAR10(root="../data",train=True,transform=torchvision.transforms.ToTensor(),
                                          download=True)
#测试数据集
test_data = torchvision.datasets.CIFAR10(root="../data",train=False,transform=torchvision.transforms.ToTensor(),
                                         download=True)
#查看数据集中有多少图片，长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度为:{}".format(train_data_size))
print("测试数据集长度为{}".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
tudui = Tudui()

#损失函数
loss_fn = nn.CrossEntropyLoss()

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

#训练网络的一些参数
#训练次数
total_train_step = 0
#测试次数
total_test_step = 0
#训练轮数
epoch = 10
#循环10次
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 = tudui(imgs)
        loss = loss_fn(outputs,targets) #损失函数
        
        #优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_train_step = total_test_step+1
        print("训练次数：{}，Loss:{}".format(total_train_step,loss))
        
        

训练步骤02

import torchvision
from torch.utils.data import DataLoader
from torch import nn
from model import *

from torch.utils.tensorboard import SummaryWriter
#训练数据集
train_data = torchvision.datasets.CIFAR10(root="../data",train=True,transform=torchvision.transforms.ToTensor(),
                                          download=True)
#测试数据集
test_data = torchvision.datasets.CIFAR10(root="../data",train=False,transform=torchvision.transforms.ToTensor(),
                                         download=True)
#查看数据集中有多少图片，长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度为:{}".format(train_data_size))
print("测试数据集长度为{}".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
tudui = Tudui()

#损失函数
loss_fn = nn.CrossEntropyLoss()

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

#训练网络的一些参数
#训练次数
total_train_step = 0
#测试次数
total_test_step = 0
#训练轮数
epoch = 10

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


#循环10次
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 = tudui(imgs)
        loss = loss_fn(outputs,targets) #损失函数
        
        #优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_train_step = total_test_step+1
        if total_train_step % 100 ==0:
            print("训练次数：{}，Loss:{}".format(total_train_step,loss))
            writer.add_scalar("tranin_loss",loss.item(),total_train_step)
        
     #测试步骤
    with torch.no_grad():
        
           for data in test_dataloader:
               imgs,targets = data
               outputs = tudui(imgs)
               loss = loss_fn(outputs,targets)
               total_test_loss = total_test_step +loss.item()
    print("整体测试集上的Loss:{}".format(total_test_loss))
    writer.add_scalar("test_loss",total_test_loss,total_test_step)
    total_test_step = total_test_step + 1