卷积神经网络框架实现mnist数据集分类
时间: 2023-11-13 17:07:27 浏览: 41
好的,下面我将介绍使用PyTorch框架实现MNIST数据集分类的卷积神经网络。在开始之前,请确保已经安装了PyTorch。
首先,导入必要的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
```
接着,我们可以定义一些超参数:
```python
batch_size = 64
learning_rate = 0.01
num_epochs = 10
```
然后,我们可以使用PyTorch内置的transforms模块来对数据进行预处理,包括将数据转换为张量、数据归一化等操作:
```python
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
```
接下来,我们可以使用datasets模块来加载MNIST数据集:
```python
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)
```
然后,我们可以使用DataLoader模块来创建数据加载器:
```python
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
```
接着,我们可以定义卷积神经网络模型:
```python
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
self.fc1 = nn.Linear(64*7*7, 1024)
self.fc2 = nn.Linear(1024, 10)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.pool(x)
x = self.conv2(x)
x = self.relu(x)
x = self.pool(x)
x = x.view(-1, 64*7*7)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
```
这里我们定义了一个包含两个卷积层和两个全连接层的卷积神经网络。
接下来,我们可以实例化模型,并定义损失函数和优化器:
```python
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
```
然后,我们可以开始训练模型:
```python
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
% (epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.item()))
```
最后,我们可以计算模型在测试集上的准确率:
```python
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the 10000 test images: %.2f %%' % (100 * correct / total))
```
这样就完成了使用PyTorch框架实现MNIST数据集分类的卷积神经网络。