Bi-LSTM的理解以及 Tensorflow实现

最新推荐文章于 2025-07-12 16:35:50 发布

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最新推荐文章于 2025-07-12 16:35:50 发布 · 1.6w 阅读

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Bidirectional LSTM，由两个LSTMs上下叠加在一起组成。输出由这两个LSTMs的隐藏层的状态决定。

def bilstm(self,x):
        
        # 输入的数据格式转换
        # x.shape [batch_size, time_steps, input_size]
        x=tf.transpose(x,[1,0,2])
        fw_x = tf.reshape(x, [-1, self.n_input_text]) # step*batch, feature
        fw_x = tf.split(0, self.n_step_text, fw_x)
        
    
        with tf.variable_scope('bilstm_lt'):
            
            #定义Cell,单层LSTM
            lstm_fw_cell = rnn_cell.BasicLSTMCell(self.n_hidden_text, forget_bias=1.0, state_is_tuple=True)#前向的lstm cell
            lstm_bw_cell = rnn_cell.BasicLSTMCell(self.n_hidden_text, forget_bias=1.0, state_is_tuple=True)#反向的rnn cell
            
            #dropout
            lstm_fw_cell = rnn_cell.DropoutWrapper(cell=lstm_fw_cell, input_keep_prob=1.0, output_keep_prob=keep_prob)