.EOS_IDX

`.EOS_IDX`通常是用于表示序列结束的标记（End of Sequence）的整数值。在自然语言处理任务中，尤其是机器翻译任务中，通常会在目标序列的末尾加上一个特殊的标记，表示该序列的结束。

例如，在使用神经机器翻译模型时，可以使用一个整数值表示目标序列的结束。假设我们已经定义了一个Vocabulary类，其中包含了源语言和目标语言的单词表，并且将目标语言的结束标记定义为整数值1000，可以使用如下代码访问该结束标记：

EOS_IDX = vocab.target_vocab['<EOS>']

在这个例子中，`vocab.target_vocab`是一个字典，其中包含了目标语言的单词表。`vocab.target_vocab['<EOS>']`返回了目标语言的结束标记的整数值1000。

在实际应用中，可以使用`.EOS_IDX`作为一个特殊的标记，将其添加到目标序列的末尾，以表示该序列的结束。

相关问题

def translate(model, src, data_loader, config): src_vocab = data_loader.de_vocab tgt_vocab = data_loader.en_vocab src_tokenizer = data_loader.tokenizer['de'] model.eval() tokens = [src_vocab.stoi[tok] for tok in src_tokenizer(src)] # 构造一个样本 num_tokens = len(tokens) src = (torch.LongTensor(tokens).reshape(num_tokens, 1)) # 将src_len 作为第一个维度 with torch.no_grad(): tgt_tokens = greedy_decode(model, src, max_len=num_tokens + 5, start_symbol=data_loader.BOS_IDX, config=config, data_loader=data_loader).flatten() # 解码的预测结果 return " ".join([tgt_vocab.itos[tok] for tok in tgt_tokens]).replace("<bos>", "").replace("<eos>", "")

这段代码是什么意思？

这段代码是一个使用seq2seq模型进行翻译的函数。它需要一个seq2seq模型，一个源语言句子（src），一个数据加载器（data_loader）和一个配置文件（config）作为输入。

首先，从数据加载器中获取源语言词表（src_vocab）、目标语言词表（tgt_vocab）和源语言分词器（src_tokenizer）。然后，将源语言句子分词，将每个单词转化为其在词表中的索引，构成token序列tokens。

接下来，将token序列tokens转化为PyTorch的LongTensor，并reshape成形状为(num_tokens, 1)的张量，其中num_tokens为token序列的长度。这个张量作为模型的输入，调用greedy_decode函数进行解码，得到目标语言的token序列tgt_tokens。

最后，将目标语言的token序列tgt_tokens转化为单词序列，通过join函数连接单词，将结果作为翻译结果返回。在转化为单词序列之前，需要将BOS（Beginning of Sentence）和EOS（End of Sentence）标记从序列中删除。

改进transformer基本翻译模型代码

以下是改进后的Transformer基本翻译模型代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader

class Transformer(nn.Module):
    def __init__(self, input_dim, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)

        self.layers = nn.ModuleList([TransformerBlock(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])

        self.fc_out = nn.Linear(hid_dim, output_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, trg, src_mask, trg_mask):
        # src = [batch size, src len]
        # trg = [batch size, trg len]
        # src_mask = [batch size, 1, 1, src len]
        # trg_mask = [batch size, 1, trg len, trg len]

        batch_size = trg.shape[0]
        trg_len = trg.shape[1]
        src_len = src.shape[1]

        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(device)

        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))

        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(device)

        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))

        for layer in self.layers:
            trg, _ = layer(trg, src, trg_mask, src_mask)

        output = self.fc_out(trg)

        return output

class TransformerBlock(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, trg, src, trg_mask, src_mask):
        # trg = [batch size, trg len, hid dim]
        # src = [batch size, src len, hid dim]
        # trg_mask = [batch size, 1, trg len, trg len]
        # src_mask = [batch size, 1, 1, src len]

        # self attention
        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)

        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))

        # encoder attention
        _trg, attention = self.encoder_attention(trg, src, src, src_mask)

        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))

        # positionwise feedforward
        _trg = self.positionwise_feedforward(trg)

        trg = self.ff_layer_norm(trg + self.dropout(_trg))

        return trg, attention

class MultiHeadAttentionLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout, device):
        super().__init__()

        assert hid_dim % n_heads == 0

        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads

        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)

        self.fc_o = nn.Linear(hid_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)

    def forward(self, query, key, value, mask = None):
        batch_size = query.shape[0]

        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)

        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)

        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale

        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)

        attention = torch.softmax(energy, dim = -1)

        x = torch.matmul(self.dropout(attention), V)

        x = x.permute(0, 2, 1, 3).contiguous()

        x = x.view(batch_size, -1, self.hid_dim)

        x = self.fc_o(x)

        return x, attention

class PositionwiseFeedforwardLayer(nn.Module):
    def __init__(self, hid_dim, pf_dim, dropout):
        super().__init__()

        self.fc_1 = nn.Linear(hid_dim, pf_dim)
        self.fc_2 = nn.Linear(pf_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # x = [batch size, seq len, hid dim]

        x = self.dropout(torch.relu(self.fc_1(x)))

        x = self.fc_2(x)

        # x = [batch size, seq len, hid dim]

        return x

class TranslationDataset(Dataset):
    def __init__(self, src_sentences, trg_sentences, src_vocab, trg_vocab):
        self.src_sentences = src_sentences
        self.trg_sentences = trg_sentences
        self.src_vocab = src_vocab
        self.trg_vocab = trg_vocab

    def __len__(self):
        return len(self.src_sentences)

    def __getitem__(self, idx):
        src_sentence = self.src_sentences[idx]
        trg_sentence = self.trg_sentences[idx]

        src_indexes = [self.src_vocab.stoi["<sos>"]] + [self.src_vocab.stoi[word] for word in src_sentence] + [self.src_vocab.stoi["<eos>"]]
        trg_indexes = [self.trg_vocab.stoi["<sos>"]] + [self.trg_vocab.stoi[word] for word in trg_sentence] + [self.trg_vocab.stoi["<eos>"]]

        return {"src": src_indexes, "trg": trg_indexes}

def train(model, iterator, optimizer, criterion, clip):
    model.train()

    epoch_loss = 0

    for i, batch in enumerate(iterator):
        src = batch["src"]
        trg = batch["trg"]

        src_mask = (src != SRC.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(2)

        trg_mask = (trg != TRG.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(3)

        trg_len = trg.shape[1]

        trg_pad_mask = torch.ones((batch_size, 1, trg_len, trg_len), device = device)

        trg_pad_mask = trg_pad_mask & trg_mask

        optimizer.zero_grad()

        output = model(src, trg[:,:-1], src_mask, trg_pad_mask[:,:-1,:-1,:])

        output_dim = output.shape[-1]

        output = output.contiguous().view(-1, output_dim)

        trg = trg[:,1:].contiguous().view(-1)

        loss = criterion(output, trg)

        loss.backward()

        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)

        optimizer.step()

        epoch_loss += loss.item()

    return epoch_loss / len(iterator)

def evaluate(model, iterator, criterion):
    model.eval()

    epoch_loss = 0

    with torch.no_grad():
        for i, batch in enumerate(iterator):
            src = batch["src"]
            trg = batch["trg"]

            src_mask = (src != SRC.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(2)

            trg_mask = (trg != TRG.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(3)

            trg_len = trg.shape[1]

            trg_pad_mask = torch.ones((batch_size, 1, trg_len, trg_len), device = device)

            trg_pad_mask = trg_pad_mask & trg_mask

            output = model(src, trg[:,:-1], src_mask, trg_pad_mask[:,:-1,:-1,:])

            output_dim = output.shape[-1]

            output = output.contiguous().view(-1, output_dim)

            trg = trg[:,1:].contiguous().view(-1)

            loss = criterion(output, trg)

            epoch_loss += loss.item()

    return epoch_loss / len(iterator)

def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
    model.eval()

    if isinstance(sentence, str):
        nlp = spacy.load("en_core_web_sm")
        tokens = [token.text.lower() for token in nlp(sentence)]
    else:
        tokens = [token.lower() for token in sentence]

    tokens = [src_field.init_token] + tokens + [src_field.eos_token]

    src_indexes = [src_field.vocab.stoi[token] for token in tokens]

    src_tensor = torch.LongTensor(src_indexes).unsqueeze(0).to(device)

    src_mask = (src_tensor != src_field.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(2)

    with torch.no_grad():
        enc_src = model.tok_embedding(src_tensor) * model.scale
        enc_src += model.pos_embedding(torch.arange(0, src_tensor.shape[1]).unsqueeze(0).to(device))
        for layer in model.layers:
            enc_src, _ = layer(enc_src, enc_src, src_mask, src_mask)

    trg_indexes = [trg_field.vocab.stoi[trg_field.init_token]]

    for i in range(max_len):
        trg_tensor = torch.LongTensor([trg_indexes[-1]]).unsqueeze(0).to(device)

        trg_mask = (trg_tensor != trg_field.vocab.stoi["<pad>"]).unsqueeze(1).unsqueeze(2)

        with torch.no_grad():
            output, attention = model(enc_src, trg_tensor, src_mask, trg_mask)

        pred_token = output.argmax(2)[:,-1].item()

        trg_indexes.append(pred_token)

        if pred_token == trg_field.vocab.stoi[trg_field.eos_token]:
            break

    trg_tokens = [trg_field.vocab.itos[i] for i in trg_indexes]

    return trg_tokens[1:], attention

# 定义超参数
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
HID_DIM = 256
N_LAYERS = 3
N_HEADS = 8
PF_DIM = 512
DROPOUT = 0.1
BATCH_SIZE = 128
CLIP = 1

# 初始化模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Transformer(INPUT_DIM, OUTPUT_DIM, HID_DIM, N_LAYERS, N_HEADS, PF_DIM, DROPOUT, device).to(device)

# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=0.0005)
criterion = nn.CrossEntropyLoss(ignore_index = TRG.vocab.stoi["<pad>"])

# 定义训练和验证数据集
train_dataset = TranslationDataset(train_src_sentences, train_trg_sentences, SRC.vocab, TRG.vocab)
valid_dataset = TranslationDataset(valid_src_sentences, valid_trg_sentences, SRC.vocab, TRG.vocab)

# 定义训练和验证数据集的迭代器
train_iterator = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
valid_iterator = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=True)

# 训练模型
N_EPOCHS = 10
CLIP = 1

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):
    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
    valid_loss = evaluate(model, valid_iterator, criterion)

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'tut6-model.pt')

    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Val. Loss: {valid_loss:.3f}')

# 测试模型
model.load_state_dict(torch.load('tut6-model.pt'))

test_dataset = TranslationDataset(test_src_sentences, test_trg_sentences, SRC.vocab, TRG.vocab)
test_iterator = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)

test_loss = evaluate(model, test_iterator, criterion)

print(f'| Test Loss: {test_loss:.3f}')

# 翻译句子
example_idx = 12

src_sentence = ' '.join(test_src_sentences[example_idx])
trg_sentence = ' '.join(test_trg_sentences[example_idx])

translation, attention = translate_sentence(src_sentence, SRC, TRG, model, device)

print(f'src = {src_sentence}')
print(f'trg = {trg_sentence}')
print(f'predicted trg = {translation}')

# 相关问题：
--相关问题--: