用python将正序序列和逆序序列都利用 ＷｏｒｄＥｍｂｅｄｄｉｎｇ技术生成词向量，分别作为本文设计的Ａｔｔｅｎｔｉｏｎ－ＢａｓｅｄＬＳＴＭ文本分类模型的输入序列

好的，您的问题是如何使用 Python 生成正序序列和逆序序列的词向量，并作为 Attention-Based LSTM 文本分类模型的输入序列。

首先，我们需要导入所需的库：

import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Dropout, Attention
from tensorflow.keras.models import Model

接下来，我们需要加载训练好的词向量模型，这里以 GloVe 为例：

EMBEDDING_DIM = 100
MAX_SEQ_LEN = 100
MAX_NUM_WORDS = 20000

# load pre-trained word embeddings
words_list = []
with open('glove.6B.100d.txt', encoding='utf-8') as f:
    for line in f:
        values = line.split()
        word = values[0]
        words_list.append(word)

# create word-to-index dictionary
word_index = {}
for idx, word in enumerate(words_list):
    word_index[word] = idx + 1

# create embedding matrix
embedding_matrix = np.zeros((MAX_NUM_WORDS + 1, EMBEDDING_DIM))
with open('glove.6B.100d.txt', encoding='utf-8') as f:
    for line in f:
        values = line.split()
        word = values[0]
        if word in word_index:
            idx = word_index[word]
            embedding_matrix[idx] = np.asarray(values[1:], dtype=np.float32)

然后，我们可以定义模型的输入和输出：

input_seqs = Input(shape=(MAX_SEQ_LEN,), dtype='int32')

embedding_layer = Embedding(
    MAX_NUM_WORDS + 1,
    EMBEDDING_DIM,
    weights=[embedding_matrix],
    input_length=MAX_SEQ_LEN,
    trainable=False
)(input_seqs)

lstm_layer = LSTM(64, return_sequences=True)(embedding_layer)

attention_layer = Attention()([lstm_layer, lstm_layer])

dense_layer = Dense(64, activation='relu')(attention_layer)
dropout_layer = Dropout(0.5)(dense_layer)

output_layer = Dense(1, activation='sigmoid')(dropout_layer)

model = Model(inputs=input_seqs, outputs=output_layer)

接着，我们定义模型的损失函数和优化器：

model.compile(
    loss='binary_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

最后，我们可以使用 pad_sequences 函数将正序序列和逆序序列转换为定长序列，并输入模型中进行训练：

## assume X_train_pos and X_train_neg are the positive and negative training data, respectively
X_train_pos_seq = pad_sequences(X_train_pos, maxlen=MAX_SEQ_LEN)
X_train_neg_seq = pad_sequences(X_train_neg, maxlen=MAX_SEQ_LEN)

## concatenate positive and negative sequences and labels
X_train_seq = np.concatenate((X_train_pos_seq, X_train_neg_seq))
y_train = np.concatenate((np.ones(len(X_train_pos_seq)), np.zeros(len(X_train_neg_seq))))

## shuffle the training data
np.random.seed(7)
shuffle_indices = np.random.permutation(np.arange(len(X_train_seq)))
X_train_seq = X_train_seq[shuffle_indices]
y_train = y_train[shuffle_indices]

## train the model
model.fit(X_train_seq, y_train, validation_split=0.1, epochs=10, batch_size=128)

希望以上内容对您有所帮助。如果您有其他问题，请随时提出。