python – tensorflow：简单LSTM网络的共享变量错误

发布时间：2020-12-20 12:10:32 所属栏目：Python 来源：网络整理

导读：我正在尝试构建一个最简单的LSTM网络.只是希望它预测序列np_input_data中的下一个值. import tensorflow as tffrom tensorflow.python.ops import rnn_cellimport numpy as npnum_steps = 3num_units = 1np_input_data = [np.array([[1.],[2.]]),np.array([[

我正在尝试构建一个最简单的LSTM网络.只是希望它预测序列np_input_data中的下一个值.

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)

口译员返回：

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

我做错了什么？

解决方法

在这里打电话给lstm：

for i in range(num_steps-1):
  output,state)

将尝试每次迭代创建具有相同名称的变量,除非您另有说明.您可以使用tf.variable_scope执行此操作

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

第一次迭代创建表示LSTM参数的变量,每次后续迭代(在调用reuse_variables之后)将只按名称在范围内查找它们.

（编辑：李大同）

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