基于transformer的文本识别中文代码

以下是基于transformer的中文文本识别代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import numpy as np
import random

# 定义超参数
batch_size = 32
max_len = 50
vocab_size = 10000
embedding_dim = 256
num_heads = 8
hidden_dim = 512
num_layers = 6
dropout = 0.1
lr = 0.0001
num_epochs = 10

# 定义数据集
class TextDataset(data.Dataset):
    def __init__(self, data_file):
        self.data = []
        with open(data_file, 'r', encoding='utf-8') as f:
            for line in f:
                self.data.append(line.strip())
                
    def __getitem__(self, index):
        text = self.data[index]
        text = text[:max_len]
        text = [int(x) for x in text]
        text = torch.LongTensor(text)
        return text
    
    def __len__(self):
        return len(self.data)

# 定义模型
class TransformerModel(nn.Module):
    def __init__(self):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.pos_embedding = nn.Embedding(max_len, embedding_dim)
        self.encoder_layers = nn.ModuleList([
            nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=num_heads, dim_feedforward=hidden_dim, dropout=dropout)
            for _ in range(num_layers)
        ])
        self.encoder = nn.TransformerEncoder(self.encoder_layers, num_layers)
        self.decoder = nn.Linear(embedding_dim, vocab_size)
        
    def forward(self, x):
        x = self.embedding(x)
        pos = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0).repeat(x.shape[0], 1)
        pos = self.pos_embedding(pos)
        x = x + pos
        x = x.permute(1, 0, 2)
        x = self.encoder(x)
        x = x.permute(1, 0, 2)
        x = self.decoder(x)
        return x
        
# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    total_loss = 0
    for i, data in enumerate(train_loader):
        optimizer.zero_grad()
        inputs = data[:, :-1]
        targets = data[:, 1:]
        outputs = model(inputs)
        outputs = outputs.permute(0, 2, 1)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    total_loss = 0
    with torch.no_grad():
        for i, data in enumerate(test_loader):
            inputs = data[:, :-1]
            targets = data[:, 1:]
            outputs = model(inputs)
            outputs = outputs.permute(0, 2, 1)
            loss = criterion(outputs, targets)
            total_loss += loss.item()
    return total_loss / len(test_loader)

# 加载数据
train_data = TextDataset('train.txt')
test_data = TextDataset('test.txt')
train_loader = data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = data.DataLoader(test_data, batch_size=batch_size, shuffle=False)

# 定义模型、损失函数和优化器
model = TransformerModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 开始训练
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, criterion, optimizer)
    test_loss = test(model, test_loader, criterion)
    print('epoch [{}/{}], train_loss:{:.4f}, test_loss:{:.4f}'.format(epoch+1, num_epochs, train_loss, test_loss))

这里用了一个简单的字符级别的文本生成任务作为示例。该任务的目标是给定一些文本的前缀，预测下一个字符是什么。训练集和测试集的文件格式如下：

12345678912345678912345678912345678912345678912345
98765432198765432198765432198765432198765432198765
...

其中每行都是一个由数字组成的序列。在模型中，我们使用transformer编码器来对输入进行编码，并使用全连接层进行预测。在训练过程中，我们使用交叉熵损失函数和Adam优化器。