本文主要是介绍Pytorch 入门之-- 逻辑回归 Logistic_Regression，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！
代码+注释
__author__ = 'SherlockLiao'import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
import time
# 定义超参数
batch_size = 32
learning_rate = 1e-3
num_epoches = 100# 下载训练集 MNIST 手写数字训练集，不需要下载的话download=FALSE
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=TRUE)test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)# 定义 Logistic Regression 模型
# pytorch的基本框架init forward class Logstic_Regression(nn.Module):def __init__(self, in_dim, n_class):super(Logstic_Regression, self).__init__()self.logstic = nn.Linear(in_dim, n_class)def forward(self, x):out = self.logstic(x)return out#模型实例化
model = Logstic_Regression(28 * 28, 10) # 图片大小是28x28
use_gpu = torch.cuda.is_available() # 判断是否有GPU加速
if use_gpu:model = model.cuda()# 定义loss和optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)# 开始训练
for epoch in range(num_epoches):print('*' * 10)print('epoch {}'.format(epoch + 1))since = time.time()running_loss = 0.0running_acc = 0.0for i, data in enumerate(train_loader, 1):#迭代数据img, label = dataimg = img.view(img.size(0), -1) # 将图片展开成 28x28if use_gpu:img = Variable(img).cuda()label = Variable(label).cuda()else:img = Variable(img)label = Variable(label)# 向前传播out = model(img)loss = criterion(out, label)#解决方法：把 train_loss += loss.data[0] 修改为 train_loss += loss.item()running_loss += loss.item()* label.size(0)_, pred = torch.max(out, 1)num_correct = (pred == label).sum()running_acc += num_correct.item()# 向后传播optimizer.zero_grad()loss.backward()optimizer.step()if i % 300 == 0:print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(epoch + 1, num_epoches, running_loss / (batch_size * i),running_acc / (batch_size * i)))
print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(train_dataset))))model.eval()
eval_loss = 0.
eval_acc = 0.
for data in test_loader:img, label = dataimg = img.view(img.size(0), -1)if use_gpu:img = Variable(img, volatile=True).cuda()label = Variable(label, volatile=True).cuda()else:img = Variable(img, volatile=True)label = Variable(label, volatile=True)out = model(img)loss = criterion(out, label)eval_loss += loss.item() * label.size(0)_, pred = torch.max(out, 1)num_correct = (pred == label).sum()eval_acc += num_correct.data[0]
print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_dataset)), eval_acc / (len(test_dataset))))
print('Time:{:.1f} s'.format(time.time() - since))
print()# 保存模型
torch.save(model.state_dict(), './logstic.pth')
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