ValueError異常：嘗試以比其第一次使用

Question

下面的代碼片斷

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) 

產生以下錯誤不同的變量範圍重用RNNCell：

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.)

如何解決這個問題？

我的Tensorflow版本是1.0。

Answer 1

正如評論我的解決方案是建議：
改變這個

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") 

到：

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) 

這似乎解決了重用性的問題。我並沒有從根本上理解底層的問題，但是這個解決了我在TF 1.1rc2
歡呼聲中的問題！