写一段基于Keras的bilstm-attention代码

好的，以下是一个基于Keras的bilstm-attention代码：

from keras.layers import Input, Embedding, LSTM, Bidirectional, Dense, Dropout, TimeDistributed, Lambda
from keras.models import Model
import keras.backend as K

# 定义输入层
inputs = Input(shape=(MAX_SEQUENCE_LENGTH,))

# 定义嵌入层
embedding_layer = Embedding(input_dim=MAX_NUM_WORDS, output_dim=EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH)(inputs)

# 定义BiLSTM层
lstm_layer = Bidirectional(LSTM(64, return_sequences=True))(embedding_layer)

# 定义Attention层
attention = TimeDistributed(Dense(1, activation='tanh'))(lstm_layer)
attention = Lambda(lambda x: K.squeeze(x, axis=-1))(attention)
attention = Lambda(lambda x: K.softmax(x, axis=-1))(attention)
attention = RepeatVector(128)(attention)
attention = Permute([2, 1])(attention)

# 对BiLSTM层的输出进行加权
sent_representation = multiply([lstm_layer, attention])
sent_representation = Lambda(lambda xin: K.sum(xin, axis=-2), output_shape=(128,))(sent_representation)

# 定义全连接层
dense_layer = Dense(64, activation='relu')(sent_representation)
dense_layer = Dropout(0.5)(dense_layer)
outputs = Dense(1, activation='sigmoid')(dense_layer)

# 定义模型
model = Model(inputs=inputs, outputs=outputs)

这个模型使用了Embedding层、双向LSTM层、TimeDistributed层、Lambda层等，其中Attention层是自定义的。在这个模型中，我们先将输入通过嵌入层进行词嵌入，然后输入到BiLSTM层中，再通过Attention层对BiLSTM层的输出进行加权，得到句子的表示，最后通过全连接层将句子的表示映射到一个二元分类的输出。