Question

我试图通过为每个rnn_cell.LSTMCell定义两个变量范围，同时运行两个RNN并将它们的输出连接在一起。为什么我收到此变量已存在错误？

ValueError：变量hidden / RNN / LSTMCell / W_0已存在，不允许。你的意思是在VarScope中设置reuse = True吗？

为什么“hidden / RNN / LSTMCell / W_0”不是“hidden / forward_lstm_cell / RNN / LSTMCell / W_0”???

    with tf.variable_scope('hidden', reuse=reuse): #reuse=None during training
        with tf.variable_scope('forward_lstm_cell'):
            lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(num_units=self.num_hidden, use_peepholes=False, 
                                                initializer=tf.random_uniform_initializer(-0.003, 0.003),
                                                state_is_tuple=True)
            if not reuse:
                lstm_fw_cell = tf.nn.rnn_cell.DropoutWrapper(cell=lstm_fw_cell, input_keep_prob=0.7)

        with tf.variable_scope('backward_lstm_cell'):
            lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(num_units=self.num_hidden, use_peepholes=False, 
                                                forget_bias=0.0, 
                                                initializer=tf.random_uniform_initializer(-0.003, 0.003),
                                                state_is_tuple=True)
            if not reuse:
                lstm_bw_cell = tf.nn.rnn_cell.DropoutWrapper(cell=lstm_bw_cell, input_keep_prob=0.7)

        with tf.name_scope("forward_lstm"):
            outputs_fw, output_states_fw  = tf.nn.dynamic_rnn(
                cell=lstm_fw_cell,
                inputs=embed_inputs_fw,
                dtype=tf.float32,
                sequence_length=self.seq_len_l
                )

        with tf.name_scope("backward_lstm"):
            outputs_bw, output_states_bw  = tf.nn.dynamic_rnn(
                cell=lstm_bw_cell,
                inputs=embed_inputs_bw,
                dtype=tf.float32,
                sequence_length=self.seq_len_r
                )

Answer 1

只需使用tf.variable_scope代替tf.name_scope。 tf.name_scope不会为创建的变量with tf.get_variable()添加前缀。

Tensorflow运行两个RNN：变量hidden / RNN / LSTMCell / W_0已经存在

1 个答案: