python代码生成language model举例

下面是一个简单的Python示例，用于生成语言模型的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import WikiText2
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torch.nn.utils import clip_grad_norm_

# 加载WikiText-2数据集
train_dataset, valid_dataset, test_dataset = WikiText2.splits(text_field='text')

# 建立词汇表
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, iter(train_dataset)), specials=['<unk>', '<pad>', '<bos>', '<eos>'], min_freq=10)

# 定义模型
class LanguageModel(nn.Module):
    def __init__(self, vocab_size, embedding_size, hidden_size, num_layers, dropout):
        super(LanguageModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_size)
        self.lstm = nn.LSTM(embedding_size, hidden_size, num_layers, dropout=dropout)
        self.fc = nn.Linear(hidden_size, vocab_size)

    def forward(self, x, hidden):
        x = self.embedding(x)
        output, hidden = self.lstm(x, hidden)
        output = self.fc(output.view(output.size(0)*output.size(1), output.size(2)))
        return output, hidden

    def init_hidden(self, batch_size):
        return (torch.zeros(self.num_layers, batch_size, self.hidden_size),
                torch.zeros(self.num_layers, batch_size, self.hidden_size))

# 定义超参数
batch_size = 32
embedding_size = 128
hidden_size = 256
num_layers = 2
dropout = 0.2
lr = 0.001
epochs = 10

# 初始化模型、损失函数和优化器
model = LanguageModel(len(vocab), embedding_size, hidden_size, num_layers, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
    model.train()
    hidden = model.init_hidden(batch_size)
    for i, batch in enumerate(train_loader):
        x, y = batch.text.t().contiguous().cuda(), batch.target.t().contiguous().cuda()
        hidden = tuple(h.detach() for h in hidden)
        optimizer.zero_grad()
        output, hidden = model(x, hidden)
        loss = criterion(output, y.view(-1))
        loss.backward()
        clip_grad_norm_(model.parameters(), 0.5)
        optimizer.step()
        if i % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, i+1, len(train_loader), loss.item()))

# 使用模型生成文本
model.eval()
hidden = model.init_hidden(1)
x = torch.tensor([vocab.stoi['<bos>']]).unsqueeze(1).cuda()
tokens = []
for i in range(100):
    output, hidden = model(x, hidden)
    output = output[-1,:].div(0.8).exp().cpu()
    token = torch.multinomial(output, 1)[0]
    x.fill_(token)
    tokens.append(token)
text = ' '.join([vocab.itos[token] for token in tokens])
print(text)

这段代码使用 PyTorch 和 TorchText 库加载 WikiText-2 数据集，并使用 torchtext.vocab 库建立词汇表。然后，它定义了一个语言模型类，包括嵌入层、LSTM层和全连接层。接下来，它定义了超参数，初始化模型、损失函数和优化器，然后使用 DataLoader 迭代训练数据集，训练模型。最后，它使用训练好的模型生成文本。