全连接手写数字识别代码
时间: 2023-07-27 22:05:46 浏览: 32
这里给出一个使用PyTorch实现全连接神经网络进行手写数字识别的代码示例:
```python
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
# 定义全连接神经网络
class FCNet(nn.Module):
def __init__(self):
super(FCNet, self).__init__()
self.fc1 = nn.Linear(28*28, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28*28) # 将输入展开成一维向量
x = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载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())
# 定义数据加载器
batch_size = 64
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
# 定义损失函数和优化器
model = FCNet()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 测试模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
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 network on the 10000 test images: {} %'.format(100 * correct / total))
```
这个代码实现了一个有三个全连接层的神经网络模型,使用交叉熵损失函数和Adam优化器进行训练,最终在MNIST测试数据集上的准确率达到了约97%。