CNN手写字符识别pytorch
时间: 2023-11-14 15:07:50 浏览: 137
基于卷积神经网络的手写字符识别(pytorch),含可视化、训练、测试
要实现CNN手写字符识别,可以按照以下步骤进行:
1. 准备数据集:下载MNIST数据集并进行预处理,将图像转换为张量并进行归一化处理。
2. 定义模型:使用PyTorch构建CNN模型,包括卷积层、池化层、全连接层等。
3. 训练模型:定义损失函数和优化器,并使用训练集对模型进行训练。
4. 评估模型:使用测试集对模型进行评估,计算准确率等指标。
5. 使用模型:使用训练好的模型对新的手写字符图像进行识别。
以下是一个简单的CNN手写字符识别的PyTorch代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# 加载数据集
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('data', train=False, download=True, transform=transform)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 320)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return nn.functional.log_softmax(x, dim=1)
model = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
# 训练模型
def train(epoch):
model.train()
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()))
# 评估模型
def test():
model.eval()
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.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
# 加载数据集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1000, shuffle=True)
# 训练模型
for epoch in range(1, 11):
train(epoch)
test()
```
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