import torch
import torch.nn as nn
from torch.nn import Transformer
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.nn.utils import clip_grad_norm_
# 數(shù)據(jù)加載
def load_data():
    # 加載源語言數(shù)據(jù)和目標語言數(shù)據(jù)
    # 在這里你可以根據(jù)實際情況進行數(shù)據(jù)加載和預處理
    src_sentences = [...]  # 源語言句子列表
    tgt_sentences = [...]  # 目標語言句子列表
    return src_sentences, tgt_sentences
def preprocess_data(src_sentences, tgt_sentences):
    # 在這里你可以進行數(shù)據(jù)預處理，如分詞、建立詞匯表等
    # 為了簡化示例，這里直接返回原始數(shù)據(jù)
    return src_sentences, tgt_sentences
def create_vocab(sentences):
    # 建立詞匯表，并為每個詞分配一個唯一的索引
    # 這里可以使用一些現(xiàn)有的庫，如torchtext等來處理詞匯表的構(gòu)建
    word2idx = {}
    idx2word = {}
    for sentence in sentences:
        for word in sentence:
            if word not in word2idx:
                index = len(word2idx)
                word2idx[word] = index
                idx2word[index] = word
    return word2idx, idx2word
def sentence_to_tensor(sentence, word2idx):
    # 將句子轉(zhuǎn)換為張量形式，張量的每個元素表示詞語在詞匯表中的索引
    tensor = [word2idx[word] for word in sentence]
    return torch.tensor(tensor)
def collate_fn(batch):
    # 對批次數(shù)據(jù)進行填充，使每個句子長度相同
    max_length = max(len(sentence) for sentence in batch)
    padded_batch = []
    for sentence in batch:
        padded_sentence = sentence + [0] * (max_length - len(sentence))
        padded_batch.append(padded_sentence)
    return torch.tensor(padded_batch)
# 模型定義
class TranslationModel(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, embedding_size, hidden_size, num_layers, num_heads, dropout):
        super(TranslationModel, self).__init__()
        self.embedding = nn.Embedding(src_vocab_size, embedding_size)
        self.transformer = Transformer(
            d_model=embedding_size,
            nhead=num_heads,
            num_encoder_layers=num_layers,
            num_decoder_layers=num_layers,
            dim_feedforward=hidden_size,
            dropout=dropout
        )
        self.fc = nn.Linear(embedding_size, tgt_vocab_size)
    def forward(self, src_sequence, tgt_sequence):
        embedded_src = self.embedding(src_sequence)
        embedded_tgt = self.embedding(tgt_sequence)
        output = self.transformer(embedded_src, embedded_tgt)
        output = self.fc(output)
        return output
# 參數(shù)設(shè)置
src_vocab_size = 1000
tgt_vocab_size = 2000
embedding_size = 256
hidden_size = 512
num_layers = 4
num_heads = 8
dropout = 0.2
learning_rate = 0.001
batch_size = 32
num_epochs = 10
# 加載和預處理數(shù)據(jù)
src_sentences, tgt_sentences = load_data()
src_sentences, tgt_sentences = preprocess_data(src_sentences, tgt_sentences)
src_word2idx, src_idx2word = create_vocab(src_sentences)
tgt_word2idx, tgt_idx2word = create_vocab(tgt_sentences)
# 將句子轉(zhuǎn)換為張量形式
src_tensor = [sentence_to_tensor(sentence, src_word2idx) for sentence in src_sentences]
tgt_tensor = [sentence_to_tensor(sentence, tgt_word2idx) for sentence in tgt_sentences]
# 創(chuàng)建數(shù)據(jù)加載器
dataset = list(zip(src_tensor, tgt_tensor))
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)
# 創(chuàng)建模型實例
model = TranslationModel(src_vocab_size, tgt_vocab_size, embedding_size, hidden_size, num_layers, num_heads, dropout)
# 定義損失函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=learning_rate)
# 訓練模型
for epoch in range(num_epochs):
    total_loss = 0.0
    num_batches = 0
    for batch in dataloader:
        src_inputs, tgt_inputs = batch[:, :-1], batch[:, 1:]
        optimizer.zero_grad()
        output = model(src_inputs, tgt_inputs)
        loss = criterion(output.view(-1, tgt_vocab_size), tgt_inputs.view(-1))
        loss.backward()
        clip_grad_norm_(model.parameters(), max_norm=1)  # 防止梯度爆炸
        optimizer.step()
        total_loss += loss.item()
        num_batches += 1
    average_loss = total_loss / num_batches
    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {average_loss}")
# 在訓練完成后，可以使用模型進行推理和翻譯