pytorch学习教程（三）逻辑回归

本文介绍了如何使用pytorch实现逻辑回归，这里采用MNIST数据集实现，可以对比逻辑回归和线性回归的区别

步骤：

导入必要的包

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

超参数设置

# hyper-parameters
input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

数据集加载

# MNIST dataset (image and labels)
train_dataset = torchvision.datasets.MNIST(root='../../data', # 训练数据集下载的位置
                                           train=True,
                                           transform=transforms.ToTensor(),
                                           download=True)
test_dataset = torchvision.datasets.MNIST(root='../../data', # 测试数据集下载的位置
                                         train=False,
                                         transform=transforms.ToTensor())
# 数据加载器（input pipeline）
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)

创建逻辑回归模型，loss和优化器

model = nn.Linear(input_size, num_class)

# Loss and optimizer
# nn.CrossEntropyLoss() computes softmax internally
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

训练模型

total_step = len(train_loader)
for epoch in range(num_epochs):
    for i,(images, labels) in enumerate(train_loader):
        #　reshape images to (batch_size, input_size)
        images = images.reshape(-1, 28*28)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 ==0:
            print('Epoch [{}/{}], step [{}/{}], Loss:{:.4f}'
                 .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

关于：enumerate的使用

enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标，一般用在 for 循环当中。

测试模型

# 在测试阶段，我们不需要计算梯度（为了计算效率）
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-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: {} %'.format(100*correct/total))

保存模型

torch.save(model.state_dict(), 'model.ckpt')

https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/01-basics/logistic_regression