Tensorflow RNN variable_scope error

Question

我想在不使用MultiRNNCell的情况下制作多层RNN，因为我想独立更新每个图层。所以我没有使用tf.dynamic_rnn。

with tf.variable_scope("cell"): 
    with tf.variable_scope("cell_1", reuse=True): 
    cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden) 
    states_1 = cell_1.zero_state(batch_size, tf.float32) 

    with tf.variable_scope("cell_2", reuse=True): 
    cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden) 
    states_2 = cell_2.zero_state(batch_size, tf.float32) 

    with tf.variable_scope("cell_3", reuse=True): 
    cell_3 = tf.contrib.rnn.BasicLSTMCell(n_hidden) 
    states_3 = cell_3.zero_state(batch_size, tf.float32) 

outputs_1=[] 
outputs_2=[] 
outputs_3=[] 

with tf.variable_scope("architecture"): 
    for i in range(n_step): 
    output_1, states_1 = cell_1(X[:, i], states_1) 
    output_2, states_2 = cell_2(output_1, states_2) 
    output_3, states_3 = cell_3(output_2, states_3) 
    outputs_3.append(output_3) 

然后我得到这样的错误。

ValueError: Variable architecture/basic_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True in VarScope?

因此，似乎不可能在没有MultiRNNCell的情况下声明张量流中的多个单元。我该如何解决这个问题？

Answer 1

我解决了这个问题并分享了答案。

cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) 
cell2 = tf.contrib.rnn.BasicLSTMCell(n_hidden) 
cell3 = tf.contrib.rnn.BasicLSTMCell(n_hidden) 

states = cell.zero_state(batch_size, tf.float32) 
states2 = cell2.zero_state(batch_size, tf.float32) 
states3 = cell3.zero_state(batch_size, tf.float32) 

outputs=[] 
for i in range(n_step): 
    with tf.variable_scope("cell1"): 
    output, states = cell(X[:, i], states) 
    with tf.variable_scope("cell2"): 
    output2, states2 = cell2(output, states2) 
    with tf.variable_scope("cell3"): 
    output3, states3 = cell3(output2, states3) 

    outputs.append(output3)