张量流：简单 LSTM 网络的共享变量错误

我正在尝试构建一个最简单的 LSTM 网络。只是想让它预测序列中的下一个值np_input_data.

import tensorflow as tf
from tensorflow.python.ops import rnn_cell
import numpy as np

num_steps = 3
num_units = 1
np_input_data = [np.array([[1.],[2.]]), np.array([[2.],[3.]]), np.array([[3.],[4.]])]

batch_size = 2

graph = tf.Graph()

with graph.as_default():
    tf_inputs = [tf.placeholder(tf.float32, [batch_size, 1]) for _ in range(num_steps)]

    lstm = rnn_cell.BasicLSTMCell(num_units)
    initial_state = state = tf.zeros([batch_size, lstm.state_size])
    loss = 0

    for i in range(num_steps-1):
        output, state = lstm(tf_inputs[i], state)
        loss += tf.reduce_mean(tf.square(output - tf_inputs[i+1]))

with tf.Session(graph=graph) as session:
    tf.initialize_all_variables().run()

    feed_dict={tf_inputs[i]: np_input_data[i] for i in range(len(np_input_data))}

    loss = session.run(loss, feed_dict=feed_dict)

    print(loss)

解释器返回：

ValueError: Variable BasicLSTMCell/Linear/Matrix already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
    output, state = lstm(tf_inputs[i], state)

我做错了什么？

致电给lstm here:

for i in range(num_steps-1):
  output, state = lstm(tf_inputs[i], state)

除非您另有说明，否则每次迭代都会尝试创建具有相同名称的变量。您可以使用以下方法执行此操作tf.variable_scope

with tf.variable_scope("myrnn") as scope:
  for i in range(num_steps-1):
    if i > 0:
      scope.reuse_variables()
    output, state = lstm(tf_inputs[i], state)     

第一次迭代创建代表 LSTM 参数的变量，以及每次后续迭代（在调用reuse_variables）只会在范围内按名称查找它们。