机器视觉逻辑回归pytorch完整代码
时间: 2023-06-28 20:04:02 浏览: 46
以下是一个简单的机器视觉逻辑回归的PyTorch完整代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义超参数
learning_rate = 0.001
batch_size = 64
num_epochs = 10
# 加载数据集
train_data = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)
test_data = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor(), download=True)
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)
# 定义逻辑回归模型
class LogisticRegression(nn.Module):
def __init__(self, input_dim, output_dim):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, x):
out = self.linear(x)
return out
# 实例化逻辑回归模型
model = LogisticRegression(28*28, 10)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.view(-1, 28*28)
labels = labels
# 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, len(train_loader), loss.item()))
# 测试模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.view(-1, 28*28)
labels = labels
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
```
这个代码实现了一个简单的逻辑回归模型来对手写数字进行分类。它使用了PyTorch的MNIST数据集和数据加载器模块,并定义了一个LogisticRegression类来实现逻辑回归模型。在训练和测试过程中,损失函数使用交叉熵,优化器使用随机梯度下降(SGD)。最后输出了模型在测试集上的准确率。