とValueError：その最初の使用とは異なる変数のスコープとRNNCellを再利用しようと

Question

次のコード

import tensorflow as tf 
from tensorflow.contrib import rnn 

hidden_size = 100 
batch_size = 100 
num_steps = 100 
num_layers = 100 
is_training = True 
keep_prob = 0.4 

input_data = tf.placeholder(tf.float32, [batch_size, num_steps]) 
lstm_cell = rnn.BasicLSTMCell(hidden_size, forget_bias=0.0, state_is_tuple=True) 

if is_training and keep_prob < 1: 
    lstm_cell = rnn.DropoutWrapper(lstm_cell) 
cell = rnn.MultiRNNCell([lstm_cell for _ in range(num_layers)], state_is_tuple=True) 

_initial_state = cell.zero_state(batch_size, tf.float32) 

iw = tf.get_variable("input_w", [1, hidden_size]) 
ib = tf.get_variable("input_b", [hidden_size]) 
inputs = [tf.nn.xw_plus_b(i_, iw, ib) for i_ in tf.split(input_data, num_steps, 1)] 

if is_training and keep_prob < 1: 
    inputs = [tf.nn.dropout(input_, keep_prob) for input_ in inputs] 

outputs, states = rnn.static_rnn(cell, inputs, initial_state=_initial_state) 

は、次のエラーを生成します。

ValueError: Attempt to reuse RNNCell < tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.BasicLSTMCell object at 0x10210d5c0> with a different variable scope than its first use. First use of cell was with scope 'rnn/multi_rnn_cell/cell_0/basic_lstm_cell' , this attempt is with scope `'rnn/multi_rnn_cell/cell_1/basic_lstm_cell'``.

Please create a new instance of the cell if you would like it to use a different set of weights.

If before you were using: MultiRNNCell([BasicLSTMCell(...)] * num_layers) , change to: MultiRNNCell([BasicLSTMCell(...) for _ in range(num_layers)]) .

If before you were using the same cell instance as both the forward and reverse cell of a bidirectional RNN, simply create two instances (one for forward, one for reverse).

In May 2017, we will start transitioning this cell's behavior to use existing stored weights, if any, when it is called with scope=None (which can lead to silent model degradation, so this error will remain until then.)

この問題を解決するためにどのように？

私のTensorflowバージョンは1.0です。私の解決策があるコメントで示唆したように

Answer 1

：
変更この

cell = tf.contrib.rnn.LSTMCell(state_size, state_is_tuple=True) 
cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=0.8) 
rnn_cells = tf.contrib.rnn.MultiRNNCell([cell for _ in range(num_layers)], state_is_tuple = True) 
outputs, current_state = tf.nn.dynamic_rnn(rnn_cells, x, initial_state=rnn_tuple_state, scope = "layer") 

へ：

再利用性の問題を解決するようだ

def lstm_cell(): 
    cell = tf.contrib.rnn.LSTMCell(state_size, reuse=tf.get_variable_scope().reuse) 
    return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=0.8) 

rnn_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)], state_is_tuple = True) 
outputs, current_state = tf.nn.dynamic_rnn(rnn_cells, x, initial_state=rnn_tuple_state) 

。根本的な問題を根本的に理解するわけではありませんが、これはTFの私の問題を解決しました。01rc2
歓声！