请使用pytorch框架
时间: 2023-12-09 09:05:26 浏览: 62
好的,以下是使用PyTorch框架的示例代码:
1. 建立一个全连接神经网络模型:
```
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 784)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
```
2. 加载MNIST数据集:
```
import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = dsets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = dsets.MNIST(root='./data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)
```
3. 定义损失函数和优化器:
```
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
```
4. 训练模型:
```
for epoch in range(num_epochs):
running_loss = 0.0
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = net(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, running_loss/100))
running_loss = 0.0
```
5. 测试模型:
```
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
```
阅读全文