【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

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