Introduction

When you need iterative tensor logic inside TensorFlow graph execution, tf.while_loop and tf.TensorArray are often used together. The reason is simple: Python lists do not behave correctly inside graph-traced loops, while TensorArray gives TensorFlow a graph-friendly way to write values across iterations and read them back later.

Why TensorArray Exists

Inside ordinary Python code, you might append loop results to a list. That approach breaks down when TensorFlow traces the loop into a graph because Python-side mutation is not the right abstraction for graph execution.

TensorArray is TensorFlow’s mutable, loop-friendly container for tensors. You can write one value per iteration and then stack or concatenate the results at the end.

Basic tf.while_loop Pattern

A simple example writes the squares of 0 through 4 into a TensorArray:

1import tensorflow as tf
2
3limit = tf.constant(5)
4ta = tf.TensorArray(dtype=tf.int32, size=limit)
5
6def cond(i, ta):
7    return i < limit
8
9def body(i, ta):
10    ta = ta.write(i, i * i)
11    return i + 1, ta
12
13_, result_ta = tf.while_loop(cond, body, [tf.constant(0), ta])
14result = result_ta.stack()
15
16print(result)

The loop state is passed through loop_vars, which here are the loop index and the evolving TensorArray.

Important Rule: Reassign After write

TensorArray.write() returns a new TensorArray object-like handle. That means you must capture the returned value:

ta = ta.write(i, value)

Do not write code like this and expect it to work:

ta.write(i, value)

If you ignore the returned value, the updated state is not threaded through the loop correctly.

Dynamic Size and Unknown Lengths

If the final number of iterations is not known in advance, create the array with dynamic_size=True:

ta = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True)

That is useful for loops driven by data-dependent stopping conditions.

Still, if you do know the size ahead of time, a fixed-size TensorArray is often easier to reason about and may allow more optimization.

Reading and Stacking Results

At the end of the loop, convert the TensorArray back into a tensor with stack() when shapes align across iterations:

result = result_ta.stack()

If each iteration writes variable-length pieces, concat() may be more appropriate than stack(), depending on the shape layout.

Typical Use Cases

This pattern appears in:

• sequence processing where results are produced step by step
• custom recurrent logic
• graph-traced dynamic programming
• iterative decoding or search procedures

In eager mode, a plain Python loop may be simpler. tf.while_loop becomes valuable when you need the loop inside traced TensorFlow execution, such as under @tf.function.

Full Example Under @tf.function

1import tensorflow as tf
2
3@tf.function
4def cumulative_sums(x):
5    ta = tf.TensorArray(dtype=x.dtype, size=tf.shape(x)[0])
6
7    def cond(i, running, ta):
8        return i < tf.shape(x)[0]
9
10    def body(i, running, ta):
11        running = running + x[i]
12        ta = ta.write(i, running)
13        return i + 1, running, ta
14
15    _, _, ta = tf.while_loop(cond, body, [0, tf.constant(0, dtype=x.dtype), ta])
16    return ta.stack()
17
18print(cumulative_sums(tf.constant([1, 2, 3, 4])))

This example shows why TensorArray is useful: it lets the graph carry loop-produced values cleanly from one iteration to the next.

Common Pitfalls

The most common mistake is trying to append to a Python list inside a traced TensorFlow loop. That often works differently than expected because the graph cannot model ordinary Python mutation the same way.

Another issue is forgetting to reassign the return value of ta.write(). TensorArray updates are functional, not in-place in the casual Python sense.

People also choose the wrong final conversion. Use stack() when all written tensors have compatible shapes for a new leading dimension, and use concat() only when concatenation semantics are actually correct.

Finally, if loop variables change shape across iterations, tf.while_loop may require explicit shape invariants. The problem is then usually the loop state shape, not the TensorArray itself.

Summary

• 'TensorArray is the graph-friendly way to collect per-iteration tensors in TensorFlow loops.'
• Use it with tf.while_loop when Python list mutation is not appropriate.
• Always reassign the result of write().
• Use dynamic_size=True when the number of iterations is not known up front.
• Convert the final TensorArray with stack() or concat() based on the shape you need.