【python pytorch】Pytorch实现逻辑回归

2024-09-07 06:18
文章标签 python 实现 逻辑 pytorch 回归

本文主要是介绍【python pytorch】Pytorch实现逻辑回归，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

pytorch 逻辑回归学习demo:

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable# Hyper Parameters 
input_size = 784
num_classes = 10
num_epochs = 10
batch_size = 50
learning_rate = 0.001# MNIST Dataset (Images and Labels)
train_dataset = dsets.MNIST(root='./data', train=True, transform=transforms.ToTensor(),download=True)print(train_dataset)test_dataset = dsets.MNIST(root='./data', train=False, transform=transforms.ToTensor())# Dataset Loader (Input Pipline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)# Model
class LogisticRegression(nn.Module):def __init__(self, input_size, num_classes):super(LogisticRegression, self).__init__()self.linear = nn.Linear(input_size, num_classes)def forward(self, x):out = self.linear(x)return outmodel = LogisticRegression(input_size, num_classes)# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = nn.CrossEntropyLoss()  
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  # Training the Model
for epoch in range(num_epochs):for i, (images, labels) in enumerate(train_loader):images = Variable(images.view(-1, 28*28))labels = Variable(labels)# Forward + Backward + Optimizeoptimizer.zero_grad()outputs = model(images)loss = criterion(outputs, labels)loss.backward()optimizer.step()if (i+1) % 100 == 0:print ('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))# Test the Model
correct = 0
total = 0
for images, labels in test_loader:images = Variable(images.view(-1, 28*28))outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum()print('Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))# Save the Model
torch.save(model.state_dict(), 'model.pkl')

运行结果：

Epoch: [1/10], Step: [100/1200], Loss: 2.2397
Epoch: [1/10], Step: [200/1200], Loss: 2.1378
Epoch: [1/10], Step: [300/1200], Loss: 2.0500
Epoch: [1/10], Step: [400/1200], Loss: 1.9401
Epoch: [1/10], Step: [500/1200], Loss: 1.9175
Epoch: [1/10], Step: [600/1200], Loss: 1.8203
Epoch: [1/10], Step: [700/1200], Loss: 1.7322
Epoch: [1/10], Step: [800/1200], Loss: 1.6910
Epoch: [1/10], Step: [900/1200], Loss: 1.6678
Epoch: [1/10], Step: [1000/1200], Loss: 1.5577
Epoch: [1/10], Step: [1100/1200], Loss: 1.5113
Epoch: [1/10], Step: [1200/1200], Loss: 1.5671
Epoch: [2/10], Step: [100/1200], Loss: 1.4560
Epoch: [2/10], Step: [200/1200], Loss: 1.3170
Epoch: [2/10], Step: [300/1200], Loss: 1.3822
Epoch: [2/10], Step: [400/1200], Loss: 1.2793
Epoch: [2/10], Step: [500/1200], Loss: 1.4281
Epoch: [2/10], Step: [600/1200], Loss: 1.2763
Epoch: [2/10], Step: [700/1200], Loss: 1.1570
Epoch: [2/10], Step: [800/1200], Loss: 1.1050
Epoch: [2/10], Step: [900/1200], Loss: 1.1151
Epoch: [2/10], Step: [1000/1200], Loss: 1.0385
Epoch: [2/10], Step: [1100/1200], Loss: 1.0978
Epoch: [2/10], Step: [1200/1200], Loss: 1.0007
Epoch: [3/10], Step: [100/1200], Loss: 1.1849
Epoch: [3/10], Step: [200/1200], Loss: 1.0002
Epoch: [3/10], Step: [300/1200], Loss: 1.0198
Epoch: [3/10], Step: [400/1200], Loss: 0.9248
Epoch: [3/10], Step: [500/1200], Loss: 0.8974
Epoch: [3/10], Step: [600/1200], Loss: 1.1095
Epoch: [3/10], Step: [700/1200], Loss: 1.0900
Epoch: [3/10], Step: [800/1200], Loss: 1.0178
Epoch: [3/10], Step: [900/1200], Loss: 0.9809
Epoch: [3/10], Step: [1000/1200], Loss: 0.9831
Epoch: [3/10], Step: [1100/1200], Loss: 0.8701
Epoch: [3/10], Step: [1200/1200], Loss: 0.9855
Epoch: [4/10], Step: [100/1200], Loss: 0.9081
Epoch: [4/10], Step: [200/1200], Loss: 0.8791
Epoch: [4/10], Step: [300/1200], Loss: 0.7540
Epoch: [4/10], Step: [400/1200], Loss: 0.9443
Epoch: [4/10], Step: [500/1200], Loss: 0.9346
Epoch: [4/10], Step: [600/1200], Loss: 0.8974
Epoch: [4/10], Step: [700/1200], Loss: 0.8897
Epoch: [4/10], Step: [800/1200], Loss: 0.7797
Epoch: [4/10], Step: [900/1200], Loss: 0.8608
Epoch: [4/10], Step: [1000/1200], Loss: 0.9216
Epoch: [4/10], Step: [1100/1200], Loss: 0.8676
Epoch: [4/10], Step: [1200/1200], Loss: 0.9251
Epoch: [5/10], Step: [100/1200], Loss: 0.7640
Epoch: [5/10], Step: [200/1200], Loss: 0.6955
Epoch: [5/10], Step: [300/1200], Loss: 0.8431
Epoch: [5/10], Step: [400/1200], Loss: 0.8489
Epoch: [5/10], Step: [500/1200], Loss: 0.7191
Epoch: [5/10], Step: [600/1200], Loss: 0.6671
Epoch: [5/10], Step: [700/1200], Loss: 0.6980
Epoch: [5/10], Step: [800/1200], Loss: 0.6837
Epoch: [5/10], Step: [900/1200], Loss: 0.9087
Epoch: [5/10], Step: [1000/1200], Loss: 0.7784
Epoch: [5/10], Step: [1100/1200], Loss: 0.7890
Epoch: [5/10], Step: [1200/1200], Loss: 1.0480
Epoch: [6/10], Step: [100/1200], Loss: 0.5834
Epoch: [6/10], Step: [200/1200], Loss: 0.8300
Epoch: [6/10], Step: [300/1200], Loss: 0.8316
Epoch: [6/10], Step: [400/1200], Loss: 0.7249
Epoch: [6/10], Step: [500/1200], Loss: 0.6184
Epoch: [6/10], Step: [600/1200], Loss: 0.7505
Epoch: [6/10], Step: [700/1200], Loss: 0.6599
Epoch: [6/10], Step: [800/1200], Loss: 0.7170
Epoch: [6/10], Step: [900/1200], Loss: 0.6857
Epoch: [6/10], Step: [1000/1200], Loss: 0.6543
Epoch: [6/10], Step: [1100/1200], Loss: 0.5679
Epoch: [6/10], Step: [1200/1200], Loss: 0.8261
Epoch: [7/10], Step: [100/1200], Loss: 0.7144
Epoch: [7/10], Step: [200/1200], Loss: 0.7573
Epoch: [7/10], Step: [300/1200], Loss: 0.7254
Epoch: [7/10], Step: [400/1200], Loss: 0.5918
Epoch: [7/10], Step: [500/1200], Loss: 0.6959
Epoch: [7/10], Step: [600/1200], Loss: 0.7058
Epoch: [7/10], Step: [700/1200], Loss: 0.7382
Epoch: [7/10], Step: [800/1200], Loss: 0.7282
Epoch: [7/10], Step: [900/1200], Loss: 0.6750
Epoch: [7/10], Step: [1000/1200], Loss: 0.6019
Epoch: [7/10], Step: [1100/1200], Loss: 0.6615
Epoch: [7/10], Step: [1200/1200], Loss: 0.5851
Epoch: [8/10], Step: [100/1200], Loss: 0.6492
Epoch: [8/10], Step: [200/1200], Loss: 0.5439
Epoch: [8/10], Step: [300/1200], Loss: 0.6613
Epoch: [8/10], Step: [400/1200], Loss: 0.6486
Epoch: [8/10], Step: [500/1200], Loss: 0.8281
Epoch: [8/10], Step: [600/1200], Loss: 0.6263
Epoch: [8/10], Step: [700/1200], Loss: 0.6541
Epoch: [8/10], Step: [800/1200], Loss: 0.5080
Epoch: [8/10], Step: [900/1200], Loss: 0.7020
Epoch: [8/10], Step: [1000/1200], Loss: 0.6421
Epoch: [8/10], Step: [1100/1200], Loss: 0.6207
Epoch: [8/10], Step: [1200/1200], Loss: 0.9254
Epoch: [9/10], Step: [100/1200], Loss: 0.7428
Epoch: [9/10], Step: [200/1200], Loss: 0.6815
Epoch: [9/10], Step: [300/1200], Loss: 0.6418
Epoch: [9/10], Step: [400/1200], Loss: 0.7096
Epoch: [9/10], Step: [500/1200], Loss: 0.6846
Epoch: [9/10], Step: [600/1200], Loss: 0.5124
Epoch: [9/10], Step: [700/1200], Loss: 0.6300
Epoch: [9/10], Step: [800/1200], Loss: 0.6340
Epoch: [9/10], Step: [900/1200], Loss: 0.5593
Epoch: [9/10], Step: [1000/1200], Loss: 0.5706
Epoch: [9/10], Step: [1100/1200], Loss: 0.6258
Epoch: [9/10], Step: [1200/1200], Loss: 0.7627
Epoch: [10/10], Step: [100/1200], Loss: 0.5254
Epoch: [10/10], Step: [200/1200], Loss: 0.5318
Epoch: [10/10], Step: [300/1200], Loss: 0.5448
Epoch: [10/10], Step: [400/1200], Loss: 0.5634
Epoch: [10/10], Step: [500/1200], Loss: 0.6398
Epoch: [10/10], Step: [600/1200], Loss: 0.7158
Epoch: [10/10], Step: [700/1200], Loss: 0.6169
Epoch: [10/10], Step: [800/1200], Loss: 0.5641
Epoch: [10/10], Step: [900/1200], Loss: 0.5698
Epoch: [10/10], Step: [1000/1200], Loss: 0.5612
Epoch: [10/10], Step: [1100/1200], Loss: 0.5126
Epoch: [10/10], Step: [1200/1200], Loss: 0.6746
Accuracy of the model on the 10000 test images: 87 %Process finished with exit code 0

这篇关于【python pytorch】Pytorch实现逻辑回归的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！


http://www.chinasem.cn/article/1144288。

相关文章

Spring StateMachine实现状态机使用示例详解

Spring StateMachine实现状态机使用示例详解

《SpringStateMachine实现状态机使用示例详解》本文介绍SpringStateMachine实现状态机的步骤,包括依赖导入、枚举定义、状态转移规则配置、上下文管理及服务调用示例,重点解... 目录什么是状态机使用示例什么是状态机状态机是计算机科学中的​​核心建模工具​​，用于描述对象在其生命
阅读更多...
Spring Boot 结合 WxJava 实现文章上传微信公众号草稿箱与群发

Spring Boot 结合 WxJava 实现文章上传微信公众号草稿箱与群发

《SpringBoot结合WxJava实现文章上传微信公众号草稿箱与群发》本文将详细介绍如何使用SpringBoot框架结合WxJava开发工具包,实现文章上传到微信公众号草稿箱以及群发功能,... 目录一、项目环境准备1.1 开发环境1.2 微信公众号准备二、Spring Boot 项目搭建2.1 创建
阅读更多...
IntelliJ IDEA2025创建SpringBoot项目的实现步骤

IntelliJ IDEA2025创建SpringBoot项目的实现步骤

《IntelliJIDEA2025创建SpringBoot项目的实现步骤》本文主要介绍了IntelliJIDEA2025创建SpringBoot项目的实现步骤,文中通过示例代码介绍的非常详细,对大家... 目录一、创建 Spring Boot 项目1. 新建项目2. 基础配置3. 选择依赖4. 生成项目5.
阅读更多...
使用Python删除Excel中的行列和单元格示例详解

使用Python删除Excel中的行列和单元格示例详解

《使用Python删除Excel中的行列和单元格示例详解》在处理Excel数据时,删除不需要的行、列或单元格是一项常见且必要的操作,本文将使用Python脚本实现对Excel表格的高效自动化处理,感兴... 目录开发环境准备使用 python 删除 Excphpel 表格中的行删除特定行删除空白行删除含指定
阅读更多...
Linux下删除乱码文件和目录的实现方式

Linux下删除乱码文件和目录的实现方式

《Linux下删除乱码文件和目录的实现方式》：本文主要介绍Linux下删除乱码文件和目录的实现方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录linux下删除乱码文件和目录方法1方法2总结Linux下删除乱码文件和目录方法1使用ls -i命令找到文件或目录
阅读更多...
SpringBoot+EasyExcel实现自定义复杂样式导入导出

SpringBoot+EasyExcel实现自定义复杂样式导入导出

《SpringBoot+EasyExcel实现自定义复杂样式导入导出》这篇文章主要为大家详细介绍了SpringBoot如何结果EasyExcel实现自定义复杂样式导入导出功能,文中的示例代码讲解详细,... 目录安装处理自定义导出复杂场景1、列不固定，动态列2、动态下拉3、自定义锁定行/列，添加密码4、合并
阅读更多...
mybatis执行insert返回id实现详解

mybatis执行insert返回id实现详解

《mybatis执行insert返回id实现详解》MyBatis插入操作默认返回受影响行数,需通过useGeneratedKeys+keyProperty或selectKey获取主键ID,确保主键为自... 目录 两种方式获取自增 ID：1. ​​useGeneratedKeys+keyProperty（推
阅读更多...
Spring Boot集成Druid实现数据源管理与监控的详细步骤

Spring Boot集成Druid实现数据源管理与监控的详细步骤

《SpringBoot集成Druid实现数据源管理与监控的详细步骤》本文介绍如何在SpringBoot项目中集成Druid数据库连接池,包括环境搭建、Maven依赖配置、SpringBoot配置文件... 目录1. 引言1.1 环境准备1.2 Druid介绍2. 配置Druid连接池3. 查看Druid监控
阅读更多...
Python通用唯一标识符模块uuid使用案例详解

Python通用唯一标识符模块uuid使用案例详解

《Python通用唯一标识符模块uuid使用案例详解》Pythonuuid模块用于生成128位全局唯一标识符,支持UUID1-5版本,适用于分布式系统、数据库主键等场景,需注意隐私、碰撞概率及存储优... 目录简介核心功能1. UUID版本2. UUID属性3. 命名空间使用场景1. 生成唯一标识符2. 数
阅读更多...
Linux在线解压jar包的实现方式

Linux在线解压jar包的实现方式

《Linux在线解压jar包的实现方式》：本文主要介绍Linux在线解压jar包的实现方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录linux在线解压jar包解压 jar包的步骤总结Linux在线解压jar包在 Centos 中解压 jar 包可以使用 u
阅读更多...

Copyright China编程 23002807@qq.com

沪ICP备2023033072号-2