请帮我编写预测已收集的图像数据标签的pytorch卷积神经网络
时间: 2024-05-16 14:13:37 浏览: 13
好的,以下是一个简单的卷积神经网络示例,用于预测已收集的图像数据标签,使用PyTorch框架:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, datasets
# 定义数据集类
class ImageDataset(Dataset):
def __init__(self, data, labels, transform=None):
self.data = data
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, index):
img = self.data[index]
label = self.labels[index]
if self.transform:
img = self.transform(img)
return img, label
# 定义卷积神经网络模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 4 * 4, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = self.pool(nn.functional.relu(self.conv3(x)))
x = torch.flatten(x, 1)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义训练函数
def train(model, device, train_loader, optimizer, criterion):
model.train()
running_loss = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
running_loss += loss.item() * data.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
return epoch_loss
# 定义测试函数
def test(model, device, test_loader, criterion):
model.eval()
running_loss = 0.0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(output, target)
running_loss += loss.item() * data.size(0)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
epoch_loss = running_loss / len(test_loader.dataset)
epoch_acc = correct / len(test_loader.dataset)
return epoch_loss, epoch_acc
# 加载数据集
train_data = datasets.CIFAR10(root='data', train=True, download=True, transform=transforms.ToTensor())
test_data = datasets.CIFAR10(root='data', train=False, download=True, transform=transforms.ToTensor())
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=True)
# 实例化模型、优化器和损失函数
model = CNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# 训练模型
for epoch in range(10):
train_loss = train(model, device, train_loader, optimizer, criterion)
test_loss, test_acc = test(model, device, test_loader, criterion)
print('Epoch: {} \tTraining Loss: {:.6f} \tTest Loss: {:.6f} \tTest Accuracy: {:.6f}'.format(epoch+1, train_loss, test_loss, test_acc))
```
这个示例代码使用 CIFAR-10 数据集作为训练和测试数据,但可以轻松地替换为其他图像数据集。请注意,这只是一个简单的示例,更复杂的模型和更高级的技术(如数据增强和学习率调整)可以提供更好的性能。
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