tf_seq_lstm.py 文件源码

def compute_states(self,emb):
        def unpack_sequence(tensor):
            return tf.unpack(tf.transpose(tensor, perm=[1, 0, 2]))



        with tf.variable_scope("Composition",initializer=
                               tf.contrib.layers.xavier_initializer(),regularizer=
                               tf.contrib.layers.l2_regularizer(self.reg)):
            cell_fw = rnn_cell.LSTMCell(self.hidden_dim)
            cell_bw = rnn_cell.LSTMCell(self.hidden_dim)
            #tf.cond(tf.less(self.dropout
            #if tf.less(self.dropout, tf.constant(1.0)):
            cell_fw = rnn_cell.DropoutWrapper(cell_fw,
                                           output_keep_prob=self.dropout,input_keep_prob=self.dropout)
            cell_bw=rnn_cell.DropoutWrapper(cell_bw, output_keep_prob=self.dropout,input_keep_prob=self.dropout)

            #output, state = rnn.dynamic_rnn(cell,emb,sequence_length=self.lngths,dtype=tf.float32)
            outputs,_,_=rnn.bidirectional_rnn(cell_fw,cell_bw,unpack_sequence(emb),sequence_length=self.lngths,dtype=tf.float32)
            #output = pack_sequence(outputs)
        sum_out=tf.reduce_sum(tf.pack(outputs),[0])
        sent_rep = tf.div(sum_out,tf.expand_dims(tf.to_float(self.lngths),1))



        final_state=sent_rep
        return final_state