tensorflow:简单LSTM网络的共享变量错误 [英] tensorflow: shared variables error with simple LSTM network
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问题描述
我正在尝试建立一个最简单的LSTM网络.只是希望它预测序列np_input_data
中的下一个值.
I am trying to build a simplest possible LSTM network. Just want it to predict the next value in the sequence 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)
我该怎么办?
推荐答案
此处lstm
的调用:
for i in range(num_steps-1):
output, state = lstm(tf_inputs[i], state)
除非另有说明,否则
会在每次迭代时尝试创建具有相同名称的变量.您可以使用tf.variable_scope
will try to create variables with the same name each iteration unless you tell it otherwise. You can do this using 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)
第一次迭代将创建代表您的LSTM参数的变量,并且随后的每次迭代(在调用reuse_variables
之后)都将在范围内按名称查找它们.
The first iteration creates the variables that represent your LSTM parameters and every subsequent iteration (after the call to reuse_variables
) will just look them up in the scope by name.
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