写一段问答系统中双塔模型的代码

以下是基于双塔模型的问答系统的伪代码：

# 导入所需库和模块
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam

# 定义双塔模型
class SiameseModel(tf.keras.Model):
    def __init__(self):
        super(SiameseModel, self).__init__()
        self.embedding = layers.Embedding(vocab_size, embedding_dim, input_length=max_seq_len)
        self.lstm = layers.LSTM(lstm_units)
        self.dropout = layers.Dropout(dropout_rate)

    def call(self, inputs):
        x1, x2 = inputs
        x1 = self.embedding(x1)
        x1 = self.lstm(x1)
        x1 = self.dropout(x1)

        x2 = self.embedding(x2)
        x2 = self.lstm(x2)
        x2 = self.dropout(x2)

        return x1, x2

# 定义问答系统模型
class QA_Model(tf.keras.Model):
    def __init__(self):
        super(QA_Model, self).__init__()
        self.siamese = SiameseModel()
        self.fc = layers.Dense(num_classes, activation='softmax')

    def call(self, inputs):
        x1, x2 = inputs
        x1, x2 = self.siamese([x1, x2])
        x = tf.concat([x1, x2], axis=-1)
        x = self.fc(x)
        return x

# 定义损失函数和优化器
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = Adam(learning_rate=learning_rate)

# 定义评估指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
val_loss = tf.keras.metrics.Mean(name='val_loss')
val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='val_accuracy')

# 定义训练步骤
@tf.function
def train_step(model, inputs, labels):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

# 定义验证步骤
@tf.function
def val_step(model, inputs, labels):
    predictions = model(inputs)
    v_loss = loss_object(labels, predictions)

    val_loss(v_loss)
    val_accuracy(labels, predictions)

以上是一个简单的双塔模型的问答系统的伪代码，可以根据实际需求进行修改和完善。