TensorFlow while_loop 将变量转换为常量? [英] TensorFlow while_loop converts variable to constant?

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)

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