TensorFlow while_loop convert variable to constant?

Question

I am trying to update a two-dimensional tensor in a nested while_loop(). However, passing the variable to the second loop, I cannot update it with tf.assign(), because it throws this error:

ValueError: Sliced assignment is only supported for variables

Somehow it works fine if I create a variable outside of while_loop and use it only in the first loop.

How can I change my two-dimensional variable tf in the second while loop?
(I am using python 2.7 and TensorFlow 1.2)

My code is:

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)

Answer 1

@AlexandrePassos, while_loop. tf.control_dependencies() ( ). :

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)

Answer 2

tf.Variable while, -.

, tf.assign , ( lambdas, ).