中文字体识别—书和行楷,pytorch实现，代码

以下是使用PyTorch实现中文字体识别的示例代码：

首先，导入需要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from PIL import Image
import os

定义数据集类：

class ChineseFontDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.samples = []
        for char_dir in os.listdir(self.root_dir):
            for font_dir in os.listdir(os.path.join(self.root_dir, char_dir)):
                for file_name in os.listdir(os.path.join(self.root_dir, char_dir, font_dir)):
                    img_path = os.path.join(self.root_dir, char_dir, font_dir, file_name)
                    self.samples.append((img_path, char_dir, font_dir))
    
    def __len__(self):
        return len(self.samples)
    
    def __getitem__(self, idx):
        img_path, char_dir, font_dir = self.samples[idx]
        img = Image.open(img_path).convert('L')
        label = int(char_dir)
        font = 0 if font_dir == 'simkai' else 1
        if self.transform:
            img = self.transform(img)
        return img, label, font

定义模型：

class ChineseFontCNN(nn.Module):
    def __init__(self):
        super(ChineseFontCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 2)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool2(x)
        x = x.view(-1, 64 * 7 * 7)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

定义训练函数：

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target, font) in enumerate(train_loader):
        data, target, font = data.to(device), target.to(device), font.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.CrossEntropyLoss()(output, font)
        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()))

定义测试函数：

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target, font in test_loader:
            data, target, font = data.to(device), target.to(device), font.to(device)
            output = model(data)
            test_loss += nn.CrossEntropyLoss()(output, font).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(font.view_as(pred)).sum().item()

    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)))

定义数据集和数据加载器：

transform = transforms.Compose([
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_set = ChineseFontDataset('train', transform=transform)
test_set = ChineseFontDataset('test', transform=transform)

train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = DataLoader(test_set, batch_size=64)

训练和测试模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ChineseFontCNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

for epoch in range(1, 11):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

以上就是使用PyTorch实现中文字体识别的代码示例。注意，这里的代码仅仅是示例，实际应用中可能需要进行更多的调试和优化。