pytorch 图像识别
时间: 2023-10-18 19:32:02 浏览: 48
PyTorch是一个流行的深度学习框架,可用于图像识别任务。要进行图像识别,您需要准备一个数据集包含图像和相应的标签。然后,您可以使用PyTorch中的卷积神经网络(CNN)模型来训练和测试图像分类器。
以下是一个简单的示例,展示了如何使用PyTorch进行图像识别:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# 定义数据转换
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载训练集和测试集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(7*7*64, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.max_pool2d(x, 2)
x = nn.functional.relu(self.conv2(x))
x = nn.functional.max_pool2d(x, 2)
x = x.view(-1, 7*7*64)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
model = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
# 训练模型
for epoch in range(10):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
print(f"Epoch {epoch+1}: Accuracy = {accuracy}%")
```
这是一个简单的示例,使用了经典的MNIST数据集。您可以根据自己的需求修改模型架构、数据集和训练参数。希望对您有所帮助!