我正在尝试建立一个最简单的LSTM网络。只希望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?
拨打lstm
这里的电话:
for i in range(num_steps-1):
output, state = lstm(tf_inputs[i], state)
除非另有说明,否则每次迭代都会尝试使用相同的名称创建变量。您可以使用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(在调用之后reuse_variables
)都将在名称中按名称查找它们。
本文收集自互联网,转载请注明来源。
如有侵权,请联系[email protected] 删除。
我来说两句