请你用python的pytroch包写一个识别手写数字的代码
时间: 2024-05-06 10:20:11 浏览: 11
以下是一个简单的手写数字识别代码,使用PyTorch包和MNIST数据集:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
# 定义神经网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 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, 16 * 4 * 4)
x = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载MNIST数据集并进行预处理
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
trainset = MNIST(root='./data', train=True, download=True, transform=transform)
testset = MNIST(root='./data', train=False, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testloader = DataLoader(testset, batch_size=64, shuffle=False)
# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('Epoch %d, Loss: %.3f' % (epoch+1, running_loss/len(trainloader)))
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy on the 10000 test images: %d %%' % (100 * correct / total))
```
该代码使用了一个简单的卷积神经网络,训练数据集为MNIST手写数字数据集,测试数据集为训练数据集的测试集。在训练过程中,使用交叉熵损失函数和随机梯度下降优化器进行优化。在测试过程中,计算模型的准确率。