Introduction

Whether you can use a variable for num_splits in tf.split depends on what kind of variable you mean. A normal Python integer variable is fine, but a scalar TensorFlow tensor or tf.Variable is not accepted in the same way when tf.split needs to know how many output tensors to create.

The Important Distinction

This works:

1import tensorflow as tf
2
3x = tf.constant([1, 2, 3, 4, 5, 6])
4num_splits = 3
5parts = tf.split(x, num_or_size_splits=num_splits)
6
7for part in parts:
8    print(part)

num_splits is just a Python variable holding an integer. TensorFlow sees a concrete integer value and can build a list of three output tensors.

What usually does not work is making the number of outputs itself dynamic at runtime with a scalar tensor:

1import tensorflow as tf
2
3x = tf.constant([1, 2, 3, 4, 5, 6])
4num_splits = tf.constant(3)
5
6# This is the pattern that causes confusion.
7parts = tf.split(x, num_or_size_splits=num_splits)

The issue is structural. TensorFlow needs to know how many tensors the op will return. A runtime scalar tensor does not provide that in the way the API expects for output construction.

Why Dynamic Output Count Is Hard

tf.split does not just compute data values. It also defines how many output tensors exist. That is a shape-of-the-program decision, not only a shape-of-the-data decision.

If the number of outputs could change freely at runtime, downstream Python code would not know how many tensors to unpack or wire into later operations.

That is why a Python integer is acceptable: the graph or eager program can determine the number of results immediately.

Use A Python Integer When Possible

If your split count comes from configuration, command-line options, or other non-tensor control values, keep it as a plain Python integer.

1import tensorflow as tf
2
3def split_evenly(x, pieces):
4    return tf.split(x, num_or_size_splits=pieces)
5
6x = tf.constant([[1, 2], [3, 4], [5, 6], [7, 8]])
7parts = split_evenly(x, 2)
8
9for part in parts:
10    print(part)

This is the cleanest solution when the split count is known before the TensorFlow op is created.

When You Really Need Dynamic Sizes

If the number of outputs is fixed but the sizes vary, pass explicit split sizes instead of a scalar split count.

1import tensorflow as tf
2
3x = tf.constant([10, 20, 30, 40, 50, 60])
4sizes = [2, 1, 3]
5parts = tf.split(x, num_or_size_splits=sizes)
6
7for part in parts:
8    print(part)

This still returns a fixed number of tensors, three in this example. That fixed output count is the critical property.

If the sizes are derived from tensors but the list length is fixed, you can often build the sizes tensor explicitly and keep the structural count stable.

When The Output Count Is Truly Dynamic

If the number of pieces itself must vary at runtime, tf.split is usually the wrong abstraction for the surrounding program. You may need a representation that keeps the result as one tensor-like object instead of a Python list of separate tensors.

Possible alternatives are:

• keep data in a RaggedTensor
• use tf.TensorArray inside a control-flow construct
• restructure the computation so downstream code operates on batches or segments rather than on N separate returned tensors

For example, if you want segments based on boundaries, tf.RaggedTensor.from_row_splits can be a better fit than trying to make tf.split return a dynamic Python-length list.

1import tensorflow as tf
2
3values = tf.constant([10, 20, 30, 40, 50, 60])
4row_splits = tf.constant([0, 2, 3, 6])
5ragged = tf.RaggedTensor.from_row_splits(values, row_splits)
6print(ragged)

This keeps the segmented structure in one object instead of forcing the API to emit a dynamically sized list of tensors.

Even Split Rules Still Apply

When num_or_size_splits is an integer, the dimension being split must divide evenly.

1import tensorflow as tf
2
3x = tf.constant([1, 2, 3, 4, 5])
4
5# This fails because 5 is not evenly divisible by 2.
6# tf.split(x, num_or_size_splits=2)

If even division is not guaranteed, explicit sizes are safer than an integer split count.

A Practical Rule Of Thumb

Use tf.split when the number of outputs is part of the program structure and is known in Python. If the structure must change dynamically, keep the result in a tensor-like container and avoid APIs that expect Python to know how many outputs exist ahead of time.

That rule resolves most confusion around "using a variable" here.

Common Pitfalls

• Confusing a Python integer variable with a runtime tf.Variable or scalar tensor.
• Expecting tf.split to support an unknown number of output tensors at runtime.
• Using an integer split count when the dimension is not evenly divisible.
• Returning many separate tensors when a RaggedTensor or segmented representation would be cleaner.
• Designing downstream code that depends on dynamic Python-length tensor lists.

Summary

• A Python integer variable works fine for num_or_size_splits.
• A runtime tensor is different because tf.split needs to know the output structure.
• Use explicit split sizes when the number of outputs is fixed but chunk sizes differ.
• Use RaggedTensor or TensorArray when the segmentation is truly dynamic.
• Think about output structure, not only input values, when choosing tf.split.