在python中使用带有注意力层的BI LSTM进行文本分类

import tensorflow as tf
from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, Dense, Attention
from tensorflow.keras.models import Model

input_dim = 10000  # 词汇表大小
embedding_dim = 100  # 词嵌入维度
lstm_units = 64  # LSTM单元数
attention_units = 64  # 注意力层单元数
num_classes = 2  # 分类类别数

inputs = Input(shape=(None,))

embedding_layer = Embedding(input_dim, embedding_dim)(inputs)
bi_lstm = Bidirectional(LSTM(lstm_units, return_sequences=True))(embedding_layer)
attention = Attention(attention_units)(bi_lstm)
outputs = Dense(num_classes, activation='softmax')(attention)

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

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10, batch_size=32)