问题描述
我正在尝试更新嵌套 while_loop()
中的二维张量.但是,当将变量传递给第二个循环时,我无法使用 tf.assign()
更新它,因为它会引发此错误:
I'm trying to update a two dimensional tensor in a nested while_loop()
. When passing the variable to the second loop however, I cannot updated it using tf.assign()
as it throws this error:
ValueError: Sliced assignment is only supported for variables
如果我在 while_loop 之外创建变量并仅在第一个循环中使用它,它会以某种方式正常工作.
Somehow it works fine if I create the variable outside the while_loop and use it only in the first loop.
如何在第二个 while 循环中修改我的 2D tf 变量?
(我使用的是 python 2.7 和 TensorFlow 1.2)
How can I modify my 2D tf variable in the second while loop?
(I'm using python 2.7 and TensorFlow 1.2)
我的代码:
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
BATCH_SIZE = 10
LENGTH_MAX_OUTPUT = 31
it_batch_nr = tf.constant(0)
it_row_nr = tf.Variable(0, dtype=tf.int32)
it_col_nr = tf.constant(0)
cost = tf.constant(0)
it_batch_end = lambda it_batch_nr, cost: tf.less(it_batch_nr, BATCH_SIZE)
it_row_end = lambda it_row_nr, cost_matrix: tf.less(it_row_nr, LENGTH_MAX_OUTPUT+1)
def iterate_batch(it_batch_nr, cost):
cost_matrix = tf.Variable(np.ones((LENGTH_MAX_OUTPUT+1, LENGTH_MAX_OUTPUT+1)), dtype=tf.float32)
it_rows, cost_matrix = tf.while_loop(it_row_end, iterate_row, [it_row_nr, cost_matrix])
cost = cost_matrix[0,0] # IS 1.0, SHOULD BE 100.0
return tf.add(it_batch_nr,1), cost
def iterate_row(it_row_nr, cost_matrix):
# THIS THROWS AN ERROR:
cost_matrix[0,0].assign(100.0)
return tf.add(it_row_nr,1), cost_matrix
it_batch = tf.while_loop(it_batch_end, iterate_batch, [it_batch_nr, cost])
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
out = sess.run(it_batch)
print(out)
推荐答案
在 @AlexandrePassos 的帮助下,通过将变量放置在 while_loop 之外,使此功能起作用.但是,我还必须使用 tf.control_dependencies()
强制执行命令(因为操作不直接用于循环变量).循环现在看起来像这样:
Got this to work, with @AlexandrePassos help, by placing the Variable outside the while_loop. However, I also had to force the execution of the commands using tf.control_dependencies()
(as the operations are not directly used on the loop variable). The loop now looks like this:
cost_matrix = tf.Variable(np.ones((LENGTH_MAX_OUTPUT+1, LENGTH_MAX_OUTPUT+1)), dtype=tf.float32)
def iterate_batch(it_batch_nr, cost):
it_rows = tf.while_loop(it_row_end, iterate_row, [it_row_nr])
with tf.control_dependencies([it_rows]):
cost = cost_matrix[0,0]
return tf.add(it_batch_nr,1), cost
def iterate_row(it_row_nr):
a = tf.assign(cost_matrix[0,0], 100.0)
with tf.control_dependencies([a]):
return tf.add(it_row_nr,1)
这篇关于TensorFlow while_loop 将变量转换为常量?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!