pytorch识别CIFAR10：训练ResNet-34（自定义transform，动态调整学习率，准确率提升到94.33%）
版权声明：本文为博主原创文章，欢迎转载，并请注明出处。联系方式：460356155@qq.com
前面通过数据增强，ResNet-34残差网络识别CIFAR10，准确率达到了92.6。
这里对训练过程增加2个处理：
1、训练数据集做进一步处理：对图片随机加正方形马赛克。
2、每50个epoch，学习率降低0.1倍。
代码具体修改如下：
自定义transform：
class Cutout(object):
def __init__(self, hole_size):
# 正方形马赛克的边长，像素为单位
self.hole_size = hole_size def __call__(self, img):
return cutout(img, self.hole_size) def cutout(img, hole_size):
y = np.random.randint(32)
x = np.random.randint(32) half_size = hole_size // 2 x1 = np.clip(x - half_size, 0, 32)
x2 = np.clip(x + half_size, 0, 32)
y1 = np.clip(y - half_size, 0, 32)
y2 = np.clip(y + half_size, 0, 32) imgnp = np.array(img) imgnp[y1:y2, x1:x2] = 0
img = Image.fromarray(imgnp.astype('uint8')).convert('RGB')
return img
数据集处理修改：
transform_train = transforms.Compose([
# 对原始32*32图像四周各填充4个0像素（40*40），然后随机裁剪成32*32
transforms.RandomCrop(32, padding=4), # 随机马赛克，大小为6*6
Cutout(6), # 按0.5的概率水平翻转图片
transforms.RandomHorizontalFlip(), transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) transform_test = tv.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) # 定义数据集
train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform_train)
test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform_test)
训练过程中调整学习率：
for epoch in range(1, args.epochs + 1):
if epoch % 50 == 0:
lr = args.lr * (0.1 ** (epoch // 50)) for params in optimizer.param_groups:
params['lr'] = lr net_train(net, train_load, optimizer, epoch, args.log_interval) # 每个epoch结束后用测试集检查识别准确度
net_test(net, test_load, epoch)
运行结果如下：
Files already downloaded and verified
ResNet34(
(first): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
)
(layer1): Sequential(
(0): ResBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): ResBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): ResBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): ResBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): ResBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): ResBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): ResBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): ResBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): ResBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(4): ResBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(5): ResBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): ResBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): ResBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avg_pool): AvgPool2d(kernel_size=4, stride=4, padding=0)
(fc): Linear(in_features=512, out_features=10, bias=True)
)
one epoch spend: 0:01:11.775634
EPOCH:1, ACC:44.28
one epoch spend:
0:01:12.244757
EPOCH:2, ACC:54.46
one epoch spend:
0:01:12.360205
EPOCH:3, ACC:56.84
............
one epoch spend: 0:01:19.172188
EPOCH:198, ACC:94.2
one epoch spend:
0:01:19.213334
EPOCH:199, ACC:94.19
one epoch spend:
0:01:19.222612
EPOCH:200, ACC:94.21
CIFAR10 pytorch
ResNet34 Train: EPOCH:200, BATCH_SZ:128, LR:0.1, ACC:94.33
train spend time:
4:21:32.548834
运行200个迭代，每个迭代耗时80秒，准确率提升了1.73%，达到94.33%。准确率变化曲线如下：
最新文章
- PHP中去除字符串中的换行的方法
- Android Material Design:滑动指示选项卡android.support.design.widget.TabLayout的简单使用
- [RxJS] just, return
- ABAP程序的效率(转)
- ajax读取txt文件
- JAVA之数组查询binarySearch()方法详解
- 由于抽签HT For Web ComboBox下拉框组件
- IE6下a标签失效（背景穿透）
- 进程管理之wait和waitpid
- nyoj 邮票分你一半
- 常用Java技术社区
- [Flutter] lib/main.dart:1: Warning: Interpreting this as package URI, 'package:flutter_app/main.dart'.
- strcat的用法
- C - 食物链
- 深度学习课程笔记（十一）初探 Capsule Network
- 纯css实现div中未知尺寸图片的垂直居中
- 剑指offer：数组中出现次数超过一半的数
- 如何关闭D10 启动后自动打开的 WelCome页
- 【Python】单元测试框架unitest及其高级应用
- 转：Content-disposition中Attachment和inline的区别
热门文章
- 从壹开始前后端分离 [ vue + .netcore 补充教程 ] 二八║ Nuxt 基础：面向源码研究Nuxt.js
- Ubuntu|ython3 :ImportError: cannot import name 'main'
- apriori && fpgrowth:频繁模式与关联规则挖掘
- 【Golang笔记】Golang工具包Cobra安装记录
- 一篇不一样的docker原理解析
- Python编程从入门到实践笔记——列表简介
- 【转载】 Sqlserver中通过Select Into语句快速单表备份
- Linux journalctl命令
- vue 数据改变但是视图没更新
- Spring注解IOC/DI(4)