以下是基于PyTorch的MNIST手写数字识别的步骤：
1.导入必要的库
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
```
2.定义数据预处理操作
```python
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
```
3.加载数据集
```python
train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)
test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)
```
4.定义数据加载器
```python
batch_size = 64
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
```
5.定义模型
```python
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(64*7*7, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = nn.functional.relu(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
return nn.functional.log_softmax(x, dim=1)
model = Net()
```
6.定义优化器和损失函数
```python
learning_rate = 0.01
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.5)
criterion = nn.CrossEntropyLoss()
```
7.训练模型
```python
epochs = 10
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx*len(data), len(train_loader.dataset),
100.*batch_idx/len(train_loader), loss.item()))
```
8.测试模型
```python
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
test_loss += criterion(output, target).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset), accuracy))
```
这就是基于PyTorch的MNIST手写数字识别的步骤。
最低0.47元/天 解锁文章
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
991
