ResNet残差网络Pytorch实现——cifar10数据集训练

ResNet残差网络Pytorch实现——cifar10数据集训练

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上一篇：​​【对花的种类进行批量数据预测】​​ ✌✌✌✌ ​​【目录】​​ ✌✌✌✌ 下一篇：​​【cifar10数据集的预测】​​

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大学生一枚，最近在学习神经网络，写这篇文章只是记录自己的学习历程，本文参考了​​Github上fengdu78老师的文章​​进行学习

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✌ 使用ResNet进行对cifar10数据集进行训练

import torchvision
import torch
from torchvision import transforms
import os
import json

import torch.nn as nn
import torch.optim as optim
from torchvision import transforms,datasets
from tqdm import tqdm

# 加载运算设备
device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# 数据处理
data_transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

# 每个批次的数据大小
batch_size=100

# 加载训练数据，不需要下载
train_dataset = torchvision.datasets.CIFAR10(root='./cifar10', 
                                             train=True,
                                             download=False, 
                                             transform=data_transform)

# 训练数据的加载器
train_loader = torch.utils.data.DataLoader(train_dataset, 
                                           batch_size=batch_size,
                                           shuffle=True)
                                        

# 训练数据大小 
train_num=len(train_dataset)

print('using {} images for training.'.format(train_num))

# 预测结果与真实分类的映射
cifar10_list=train_dataset.class_to_idx
cla_dict=dict((value,key) for key,value in cifar10_list.items())
json_str=json.dumps(cla_dict,indent=10)
with open('class_indices.json','w') as json_file:
    json_file.write(json_str)

# 构建网络
net=resnet34()

# 加载模型参数
model_weight_path='./resnet34-pre.pth'
net.load_state_dict(torch.load(model_weight_path,map_location=device))

# 将每个参数置为False，反向传播时不会进行梯度更新
for param in net.parameters():
    param.requires_grad=False

# 修改全连接层
in_channel=net.fc.in_features
net.fc=nn.Linear(in_channel,10)
net.to(device)

# 交叉熵损失函数
loss_function=nn.CrossEntropyLoss()

# 获得需要训练的参数
params=[p for p in net.parameters() if p.requires_grad]

# 优化器
optimizer=optim.Adam(params,lr=0.0001)

epochs=1
loss_sum=999
save_path='./resNet34_cifar10.pth'
train_steps=len(train_loader)

# 开始训练，所有数据只训练1次
for epoch in range(epochs):
    net.train()
    running_loss=0
    train_bar=tqdm(train_loader)
    
    # 训练集总共50000张图片，我设置的每批数据是100，所以对应是500*100
    # 循环500次，每次训练的数据为100张
    for data in train_bar:
        images,labels=data
        optimizer.zero_grad()
        output=net(images.to(device))
        loss=loss_function(output,labels.to(device))
        loss.backward()
        optimizer.step()
        
        running_loss+=loss.item()
        
        train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                     epochs,
                                                                     loss)
    # 保存最好的模型参数
    if running_loss/train_steps<loss_sum:
        loss_sum=running_loss/train_steps
        torch.save(net.state_dict(),save_path)