生成以pytorch为基础的minist手写数字识别
时间: 2024-05-04 12:15:51 浏览: 14
的代码,你能帮我吗?
当然可以,以下是一个基于PyTorch的手写数字识别代码:
```
import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
# 加载MNIST数据集
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 定义模型
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=5)
self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=5)
self.fc1 = torch.nn.Linear(1024, 128)
self.fc2 = torch.nn.Linear(128, 10)
def forward(self, x):
x = torch.nn.functional.relu(torch.nn.functional.max_pool2d(self.conv1(x), 2))
x = torch.nn.functional.relu(torch.nn.functional.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 1024)
x = torch.nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return torch.nn.functional.log_softmax(x, dim=1)
model = Net()
# 训练模型
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = torch.nn.functional.nll_loss(output, target)
loss.backward()
optimizer.step()
# 测试模型
def test():
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
test_loss += torch.nn.functional.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(test_loader.dataset), accuracy))
for epoch in range(1, 11):
train(epoch)
test()
```
希望对你有帮助!