神经网络实现MINST手写字符识别具体代码
时间: 2023-09-28 07:07:14 浏览: 93
下面是一个使用PyTorch实现的MINST手写字符识别的神经网络代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# 定义网络结构
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4*4*50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.max_pool2d(x, 2, 2)
x = nn.functional.relu(self.conv2(x))
x = nn.functional.max_pool2d(x, 2, 2)
x = x.view(-1, 4*4*50)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return nn.functional.log_softmax(x, dim=1)
# 加载数据集
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)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)
# 定义优化器和损失函数
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()
# 训练模型
epochs = 10
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data), Variable(target)
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.data.item()))
# 测试模型
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = Variable(data), Variable(target)
output = model(data)
test_loss += criterion(output, target).data.item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
```
以上代码实现了一个包含两个卷积层和两个全连接层的神经网络,并使用随机梯度下降算法进行优化,损失函数为交叉熵损失。在训练过程中,模型对数据进行前向传播和反向传播,更新权重参数以减小训练误差。在测试过程中,模型对测试数据进行预测,并计算准确率和平均损失。
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