使用pytorch编写
时间: 2023-07-08 16:46:05 浏览: 34
好的,以下是使用PyTorch编写的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
self.fc1 = nn.Linear(64 * 3 * 3, 64)
self.fc2 = nn.Linear(64, 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 = self.pool(nn.functional.relu(self.conv4(x)))
x = x.view(-1, 64 * 3 * 3)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
# 训练模型
net = Net()
for epoch in range(5):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy on test images: %d %%' % (100 * correct / total))
```
此代码中的模型包括四个卷积层、四个最大池化层、一个扁平层和两个全连接层。输入是28x28的MNIST图像,输出是10个类别的概率分布。代码中使用PyTorch 1.7版本。