pytorch深度学习代码实例
时间: 2023-10-19 14:09:17 浏览: 38
这是一个使用PyTorch实现的简单的深度学习代码示例,用于分类手写数字数据集MNIST:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torchsummary import summary
# 定义网络结构
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.relu2 = nn.ReLU()
self.fc1 = nn.Linear(64 * 24 * 24, 128)
self.relu3 = nn.ReLU()
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = self.relu3(x)
x = self.fc2(x)
return x
# 数据预处理
transform = ToTensor()
# 加载数据集
train_dataset = MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = MNIST(root='./data', train=False, transform=transform, download=True)
# 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)
# 实例化模型
model = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(10):
running_loss = 0.0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch + 1} loss: {running_loss / len(train_loader)}")
# 在测试集上评估模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Accuracy on test set: {100 * correct / total}%")
# 输出模型结构
summary(model, (1, 28, 28))
```