mnist手写数字识别pytorch 代码
时间: 2023-04-03 12:00:40 浏览: 137
Pytorch框架下实现的MNIST手写数字识别代码。
你好,以下是mnist手写数字识别的PyTorch代码:
```
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 定义数据预处理
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((.5,), (.5,))])
# 加载数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
# 定义神经网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 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 = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=.001, momentum=.9)
# 训练网络
for epoch in range(2): # 多次循环数据集
running_loss = .
for i, data in enumerate(trainloader, ):
# 获取输入
inputs, labels = data
# 梯度清零
optimizer.zero_grad()
# 正向传播,反向传播,优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 打印统计信息
running_loss += loss.item()
if i % 200 == 1999: # 每200个小批量数据打印一次
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 200))
running_loss = .
print('Finished Training')
# 测试网络
correct =
total =
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size()
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
希望对你有所帮助!
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