attention lstm tensorflow代码实现

以下是一个基于 TensorFlow 的 Attention LSTM 的代码实现：

import tensorflow as tf

class AttentionLSTM(tf.keras.Model):
    def __init__(self, hidden_size, attention_size):
        super(AttentionLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.attention_size = attention_size
        
        self.lstm_cell = tf.keras.layers.LSTMCell(hidden_size)
        self.attention_layer = tf.keras.layers.Dense(attention_size, activation='tanh')
        self.output_layer = tf.keras.layers.Dense(1, activation='sigmoid')
        
    def call(self, inputs, states):
        x, context = inputs
        h, c = states
        
        # Compute attention weights
        attention = self.attention_layer(tf.concat([h, context], axis=-1))
        attention = self.output_layer(attention)
        attention_weights = tf.nn.softmax(attention, axis=1)
        
        # Compute context vector
        context_vector = tf.reduce_sum(tf.multiply(context, attention_weights), axis=1)
        
        # Compute LSTM output
        lstm_input = tf.concat([x, context_vector], axis=-1)
        h, c = self.lstm_cell(lstm_input, (h, c))
        
        return h, c

    def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
        return self.lstm_cell.get_initial_state(inputs=inputs, batch_size=batch_size, dtype=dtype)

希望这个代码能够帮到你！