基于pytorch的数字识别
时间: 2023-07-25 12:22:00 浏览: 35
针对数字识别任务,可以使用PyTorch来构建深度学习模型。以下是一个基本的数字识别模型的实现:
1. 数据准备:下载MNIST数据集,并将其转换为PyTorch张量。
```python
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 下载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(), download=True)
# 转换为PyTorch张量
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)
```
2. 定义模型:定义一个包含两个卷积层和三个全连接层的卷积神经网络模型。
```python
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2)
self.fc1 = nn.Linear(7*7*64, 1024)
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(512, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = x.view(-1, 7*7*64)
x = F.relu(self.fc1(x))
x = F.dropout(x, p=0.5)
x = F.relu(self.fc2(x))
x = F.dropout(x, p=0.5)
x = self.fc3(x)
return x
model = Net()
```
3. 定义损失函数和优化器:使用交叉熵作为损失函数,使用Adam优化器进行模型训练。
```python
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
```
4. 模型训练:迭代训练模型,计算损失和准确率,并更新模型参数。
```python
num_epochs = 10
for epoch in range(num_epochs):
train_loss = 0.0
train_acc = 0.0
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
train_acc += (predicted == labels).sum().item()
train_loss /= len(train_loader)
train_acc /= len(train_loader.dataset)
print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'.format(epoch+1, num_epochs, train_loss, train_acc*100))
```
5. 模型测试:使用测试集对模型进行测试,并计算测试集的准确率。
```python
model.eval()
test_loss = 0.0
test_acc = 0.0
with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
loss = criterion(outputs, labels)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
test_acc += (predicted == labels).sum().item()
test_loss /= len(test_loader)
test_acc /= len(test_loader.dataset)
print('Test Loss: {:.4f}, Test Accuracy: {:.2f}%'.format(test_loss, test_acc*100))
```
通过以上步骤,我们可以完成一个基于PyTorch的数字识别模型的实现。