How TensorArray and while_loop work together in tensorflow?

Question

I am trying to produce a very easy example for combination of TensorArray and while_loop:

# 1000 sequence in the length of 100
matrix = tf.placeholder(tf.int32, shape=(100, 1000), name="input_matrix")
matrix_rows = tf.shape(matrix)[0]
ta = tf.TensorArray(tf.float32, size=matrix_rows)
ta = ta.unstack(matrix)

init_state = (0, ta)
condition = lambda i, _: i < n
body = lambda i, ta: (i + 1, ta.write(i,ta.read(i)*2))

# run the graph
with tf.Session() as sess:
    (n, ta_final) = sess.run(tf.while_loop(condition, body, init_state),feed_dict={matrix: tf.ones(tf.float32, shape=(100,1000))})
    print (ta_final.stack())

But I am getting the following error:

ValueError: Tensor("while/LoopCond:0", shape=(), dtype=bool) must be from the same graph as Tensor("Merge:0", shape=(), dtype=float32).

Anyone has on idea what is the problem?

To get the final TensorArray you need to session.run(ta.stack()) instead of running the loop directly which fails since you can't session.run(TensorArray). — sirfz
– sirfz, Commented May 8, 2017 at 15:38
Sorry but I did not get what you mean. Would you please write the correct form? — E.Asgari
– E.Asgari, Commented May 8, 2017 at 16:54

BoarGules · Accepted Answer · 2018-02-03 00:20:03Z

10

There are several things in your code to point out. First, you don't need to unstack the matrix into the TensorArray to use it inside the loop, you can safely reference the matrix Tensor inside the body and index it using matrix[i] notation. Another issue is the different data type between your matrix (tf.int32) and the TensorArray (tf.float32), based on your code you're multiplying the matrix ints by 2 and writing the result into the array so it should be int32 as well. Finally, when you wish to read the final result of the loop, the correct operation is TensorArray.stack() which is what you need to run in your session.run call.

Here's a working example:

import numpy as np
import tensorflow as tf    

# 1000 sequence in the length of 100
matrix = tf.placeholder(tf.int32, shape=(100, 1000), name="input_matrix")
matrix_rows = tf.shape(matrix)[0]
ta = tf.TensorArray(dtype=tf.int32, size=matrix_rows)

init_state = (0, ta)
condition = lambda i, _: i < matrix_rows
body = lambda i, ta: (i + 1, ta.write(i, matrix[i] * 2))
n, ta_final = tf.while_loop(condition, body, init_state)
# get the final result
ta_final_result = ta_final.stack()

# run the graph
with tf.Session() as sess:
    # print the output of ta_final_result
    print sess.run(ta_final_result, feed_dict={matrix: np.ones(shape=(100,1000), dtype=np.int32)})

edited Feb 3, 2018 at 0:20

BoarGules

17.1k3 gold badges29 silver badges46 bronze badges

answered May 8, 2017 at 17:16

sirfz

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3 Comments

Rahul Over a year ago

In this can I specify the input without using feed dictionary, as in if I was using this between a computational graph how would I specify that the tensor array depends on some tensor?

sirfz Over a year ago

@Rahul matrix can be any type of Tensor not necessarily a placeholder if I understood your question.

Saeid BK Over a year ago

In the last line, I changed np.ones(tf.int32, shape=(100,1000)) to np.ones(dtype=np.int32, shape=(100,1000)) to be able to run this code on python 3.

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