首页
所有分类
TAGS
文字工具 EMOJI BIBLE
编程 Python

使用PyTorch实现MNIST手写体识别代码

Posted in Python onJanuary 18, 2020

实验环境

win10 + anaconda + jupyter notebook

Pytorch1.1.0

Python3.7

gpu环境（可选）

MNIST数据集介绍

MNIST 包括6万张28x28的训练样本，1万张测试样本，可以说是CV里的“Hello Word”。本文使用的CNN网络将MNIST数据的识别率提高到了99%。下面我们就开始进行实战。

导入包

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
torch.__version__

定义超参数 

BATCH_SIZE=512
EPOCHS=20 
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

数据集

我们直接使用PyTorch中自带的dataset，并使用DataLoader对训练数据和测试数据分别进行读取。如果下载过数据集这里download可选择False

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=True, download=True, 
            transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
            ])),
    batch_size=BATCH_SIZE, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=False, transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
            ])),
    batch_size=BATCH_SIZE, shuffle=True)

定义网络

该网络包括两个卷积层和两个线性层，最后输出10个维度，即代表0-9十个数字。

class ConvNet(nn.Module):
  def __init__(self):
    super().__init__()
    self.conv1=nn.Conv2d(1,10,5) # input:(1,28,28) output:(10,24,24) 
    self.conv2=nn.Conv2d(10,20,3) # input:(10,12,12) output:(20,10,10)
    self.fc1 = nn.Linear(20*10*10,500)
    self.fc2 = nn.Linear(500,10)
  def forward(self,x):
    in_size = x.size(0)
    out = self.conv1(x)
    out = F.relu(out)
    out = F.max_pool2d(out, 2, 2) 
    out = self.conv2(out)
    out = F.relu(out)
    out = out.view(in_size,-1)
    out = self.fc1(out)
    out = F.relu(out)
    out = self.fc2(out)
    out = F.log_softmax(out,dim=1)
    return out

实例化网络 

model = ConvNet().to(DEVICE) # 将网络移动到gpu上
optimizer = optim.Adam(model.parameters()) # 使用Adam优化器

定义训练函数

def train(model, device, train_loader, optimizer, epoch):
  model.train()
  for batch_idx, (data, target) in enumerate(train_loader):
    data, target = data.to(device), target.to(device)
    optimizer.zero_grad()
    output = model(data)
    loss = F.nll_loss(output, target)
    loss.backward()
    optimizer.step()
    if(batch_idx+1)%30 == 0: 
      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        epoch, batch_idx * len(data), len(train_loader.dataset),
        100. * batch_idx / len(train_loader), loss.item()))

定义测试函数

def test(model, device, test_loader):
  model.eval()
  test_loss = 0
  correct = 0
  with torch.no_grad():
    for data, target in test_loader:
      data, target = data.to(device), target.to(device)
      output = model(data)
      test_loss += F.nll_loss(output, target, reduction='sum').item() # 将一批的损失相加
      pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标
      correct += pred.eq(target.view_as(pred)).sum().item()

  test_loss /= len(test_loader.dataset)
  print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(test_loader.dataset),
    100. * correct / len(test_loader.dataset)))

开始训练 

for epoch in range(1, EPOCHS + 1):
  train(model, DEVICE, train_loader, optimizer, epoch)
  test(model, DEVICE, test_loader)

实验结果 

Train Epoch: 1 [14848/60000 (25%)]	Loss: 0.375058
Train Epoch: 1 [30208/60000 (50%)]	Loss: 0.255248
Train Epoch: 1 [45568/60000 (75%)]	Loss: 0.128060

Test set: Average loss: 0.0992, Accuracy: 9690/10000 (97%)

Train Epoch: 2 [14848/60000 (25%)]	Loss: 0.093066
Train Epoch: 2 [30208/60000 (50%)]	Loss: 0.087888
Train Epoch: 2 [45568/60000 (75%)]	Loss: 0.068078

Test set: Average loss: 0.0599, Accuracy: 9816/10000 (98%)

Train Epoch: 3 [14848/60000 (25%)]	Loss: 0.043926
Train Epoch: 3 [30208/60000 (50%)]	Loss: 0.037321
Train Epoch: 3 [45568/60000 (75%)]	Loss: 0.068404

Test set: Average loss: 0.0416, Accuracy: 9859/10000 (99%)

Train Epoch: 4 [14848/60000 (25%)]	Loss: 0.031654
Train Epoch: 4 [30208/60000 (50%)]	Loss: 0.041341
Train Epoch: 4 [45568/60000 (75%)]	Loss: 0.036493

Test set: Average loss: 0.0361, Accuracy: 9873/10000 (99%)

Train Epoch: 5 [14848/60000 (25%)]	Loss: 0.027688
Train Epoch: 5 [30208/60000 (50%)]	Loss: 0.019488
Train Epoch: 5 [45568/60000 (75%)]	Loss: 0.018023

Test set: Average loss: 0.0344, Accuracy: 9875/10000 (99%)

Train Epoch: 6 [14848/60000 (25%)]	Loss: 0.024212
Train Epoch: 6 [30208/60000 (50%)]	Loss: 0.018689
Train Epoch: 6 [45568/60000 (75%)]	Loss: 0.040412

Test set: Average loss: 0.0350, Accuracy: 9879/10000 (99%)

Train Epoch: 7 [14848/60000 (25%)]	Loss: 0.030426
Train Epoch: 7 [30208/60000 (50%)]	Loss: 0.026939
Train Epoch: 7 [45568/60000 (75%)]	Loss: 0.010722

Test set: Average loss: 0.0287, Accuracy: 9892/10000 (99%)

Train Epoch: 8 [14848/60000 (25%)]	Loss: 0.021109
Train Epoch: 8 [30208/60000 (50%)]	Loss: 0.034845
Train Epoch: 8 [45568/60000 (75%)]	Loss: 0.011223

Test set: Average loss: 0.0299, Accuracy: 9904/10000 (99%)

Train Epoch: 9 [14848/60000 (25%)]	Loss: 0.011391
Train Epoch: 9 [30208/60000 (50%)]	Loss: 0.008091
Train Epoch: 9 [45568/60000 (75%)]	Loss: 0.039870

Test set: Average loss: 0.0341, Accuracy: 9890/10000 (99%)

Train Epoch: 10 [14848/60000 (25%)]	Loss: 0.026813
Train Epoch: 10 [30208/60000 (50%)]	Loss: 0.011159
Train Epoch: 10 [45568/60000 (75%)]	Loss: 0.024884

Test set: Average loss: 0.0286, Accuracy: 9901/10000 (99%)

Train Epoch: 11 [14848/60000 (25%)]	Loss: 0.006420
Train Epoch: 11 [30208/60000 (50%)]	Loss: 0.003641
Train Epoch: 11 [45568/60000 (75%)]	Loss: 0.003402

Test set: Average loss: 0.0377, Accuracy: 9894/10000 (99%)

Train Epoch: 12 [14848/60000 (25%)]	Loss: 0.006866
Train Epoch: 12 [30208/60000 (50%)]	Loss: 0.012617
Train Epoch: 12 [45568/60000 (75%)]	Loss: 0.008548

Test set: Average loss: 0.0311, Accuracy: 9908/10000 (99%)

Train Epoch: 13 [14848/60000 (25%)]	Loss: 0.010539
Train Epoch: 13 [30208/60000 (50%)]	Loss: 0.002952
Train Epoch: 13 [45568/60000 (75%)]	Loss: 0.002313

Test set: Average loss: 0.0293, Accuracy: 9905/10000 (99%)

Train Epoch: 14 [14848/60000 (25%)]	Loss: 0.002100
Train Epoch: 14 [30208/60000 (50%)]	Loss: 0.000779
Train Epoch: 14 [45568/60000 (75%)]	Loss: 0.005952

Test set: Average loss: 0.0335, Accuracy: 9897/10000 (99%)

Train Epoch: 15 [14848/60000 (25%)]	Loss: 0.006053
Train Epoch: 15 [30208/60000 (50%)]	Loss: 0.002559
Train Epoch: 15 [45568/60000 (75%)]	Loss: 0.002555

Test set: Average loss: 0.0357, Accuracy: 9894/10000 (99%)

Train Epoch: 16 [14848/60000 (25%)]	Loss: 0.000895
Train Epoch: 16 [30208/60000 (50%)]	Loss: 0.004923
Train Epoch: 16 [45568/60000 (75%)]	Loss: 0.002339

Test set: Average loss: 0.0400, Accuracy: 9893/10000 (99%)

Train Epoch: 17 [14848/60000 (25%)]	Loss: 0.004136
Train Epoch: 17 [30208/60000 (50%)]	Loss: 0.000927
Train Epoch: 17 [45568/60000 (75%)]	Loss: 0.002084

Test set: Average loss: 0.0353, Accuracy: 9895/10000 (99%)

Train Epoch: 18 [14848/60000 (25%)]	Loss: 0.004508
Train Epoch: 18 [30208/60000 (50%)]	Loss: 0.001272
Train Epoch: 18 [45568/60000 (75%)]	Loss: 0.000543

Test set: Average loss: 0.0380, Accuracy: 9894/10000 (99%)

Train Epoch: 19 [14848/60000 (25%)]	Loss: 0.001699
Train Epoch: 19 [30208/60000 (50%)]	Loss: 0.000661
Train Epoch: 19 [45568/60000 (75%)]	Loss: 0.000275

Test set: Average loss: 0.0339, Accuracy: 9905/10000 (99%)

Train Epoch: 20 [14848/60000 (25%)]	Loss: 0.000441
Train Epoch: 20 [30208/60000 (50%)]	Loss: 0.000695
Train Epoch: 20 [45568/60000 (75%)]	Loss: 0.000467

Test set: Average loss: 0.0396, Accuracy: 9894/10000 (99%)

总结

一个实际项目的工作流程：找到数据集，对数据做预处理，定义我们的模型，调整超参数，测试训练，再通过训练结果对超参数进行调整或者对模型进行调整。

以上这篇使用PyTorch实现MNIST手写体识别代码就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持三水点靠木。

使用PyTorch实现MNIST手写体识别代码

- Author -

kaijie234

声明：登载此文出于传递更多信息之目的，并不意味着赞同其观点或证实其描述。

Python 相关文章推荐
linux系统使用python监控apache服务器进程脚本分享
Jan 15 Python
python基础教程之Hello World!
Aug 29 Python
Python性能优化技巧
Mar 09 Python
python 查找文件名包含指定字符串的方法
Jun 05 Python
python集合比较(交集,并集,差集)方法详解
Sep 13 Python
Django unittest 设置跳过某些case的方法
Dec 26 Python
pandas DataFrame的修改方法(值、列、索引)
Aug 02 Python
python单例设计模式实现解析
Jan 07 Python
matplotlib 对坐标的控制,加图例注释的操作
Apr 17 Python
解决Opencv+Python cv2.imshow闪退问题
Apr 24 Python
Python字符串查找基本操作代码案例
Oct 27 Python
TensorFlow的自动求导原理分析
May 26 Python
Pytorch之finetune使用详解
Jan 18 #Python
pytorch 修改预训练model实例
Jan 18 #Python
Pytorch自己加载单通道图片用作数据集训练的实例
Jan 18 #Python
pyinstaller 3.6版本通过pip安装失败的解决办法(推荐)
Jan 18 #Python
Python实现点云投影到平面显示
Jan 18 #Python
Pytorch 实现计算分类器准确率(总分类及子分类)
Jan 18 #Python
在pytorch 中计算精度、回归率、F1 score等指标的实例
Jan 18 #Python
罗马尼亚购物网站(10) 哈萨克斯坦购物网站(2) 保加利亚购物网站(3) 乌克兰购物网站(27) 白俄罗斯购物网站(3) 马耳他购物网站(1) 冰岛购物网站(1) 约旦购物网站(1) PHP面试题(54) 希腊购物网站(6)
You might like
如何在PHP中使用Oracle数据库(1)
2006/10/09 PHP
php学习笔记 PHP面向对象的程序设计
2011/06/13 PHP
PHP中Restful api 错误提示返回值实现思路
2016/04/12 PHP
php 实现一个字符串加密解密的函数实例代码
2016/11/01 PHP
Lumen timezone 时区设置方法(慢了8个小时)
2018/01/20 PHP
启用OPCache提高PHP程序性能的方法
2019/03/21 PHP
JavaScript子窗口ModalDialog中操作父窗口对像
2012/12/11 Javascript
JS中获取数据库中的值的方法
2013/07/14 Javascript
解析jquery中的ajax缓存问题
2013/12/19 Javascript
使用javascript实现简单的选项卡切换
2015/01/09 Javascript
Javascript this 函数深入详解
2016/12/13 Javascript
微信小程序实现图片预加载组件
2017/01/18 Javascript
JS组件系列之MVVM组件构建自己的Vue组件
2017/04/28 Javascript
bootstrap table使用入门基本用法
2017/05/24 Javascript
vue的状态管理模式vuex
2017/11/30 Javascript
微信小程序上传图片实例
2018/05/28 Javascript
基于Webpack4和React hooks搭建项目的方法
2019/02/05 Javascript
微信小程序云函数添加数据到数据库的方法
2020/03/04 Javascript
Vue登录拦截 登录后继续跳转指定页面的操作
2020/08/04 Javascript
JavaScript实现下拉列表
2021/01/20 Javascript
[03:00]《DAC最前线》之欧美新秀VS老将
2015/02/01 DOTA
Python中的zipfile模块使用详解
2015/06/25 Python
python中私有函数调用方法解密
2016/04/29 Python
python实现简单爬虫功能的示例
2016/10/24 Python
Pycharm学习教程（7）虚拟机VM的配置教程
2017/05/04 Python
用TensorFlow实现多类支持向量机的示例代码
2018/04/28 Python
基于Python列表解析(列表推导式)
2018/06/23 Python
Python3使用pandas模块读写excel操作示例
2018/07/03 Python
django 实现电子支付功能的示例代码
2018/07/25 Python
英国最大的独立家具零售商：Furniture Village
2016/09/06 全球购物
非常详细的C#面试题集
2016/07/13 面试题
UML设计模式笔试题
2014/06/07 面试题
AJAX检测用户名是否存在的方法
2021/03/24 Javascript
毕业生物理教师求职信
2013/10/17 职场文书
实习证明格式范文
2015/06/16 职场文书
Python MNIST手写体识别详解与试练
2021/11/07 Python