以下代码片段

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)

产生以下错误:

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.)

如何解决这个问题？

我的 Tensorflow 版本是 1.0。

最佳答案

正如评论中所建议的，我的解决方案是:
改变这个

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")

进入:

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)

这似乎解决了可重用性问题。我从根本上不理解潜在的问题，但这解决了我在 TF 1.1rc2 上的问题
干杯!

关于python - 值错误 : Attempt to reuse RNNCell with a different variable scope than its first use，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42669578/