Скачать или смотреть Understanding TensorFlow Dataset Operations: The Mystery of the Equal Sign

Explore how TensorFlow Dataset handles operations and why the equal sign doesn't behave as expected. Learn about shuffle operations, data assignment, and their implications.
---
This video is based on the question https://stackoverflow.com/q/63536134/ asked by the user 'Trunway' ( https://stackoverflow.com/u/11760616/ ) and on the answer https://stackoverflow.com/a/63537171/ provided by the user 'tornikeo' ( https://stackoverflow.com/u/14142345/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Equal sign, regarding to tensorflow dataset, does not really assign values to variable

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding TensorFlow Dataset Operations: The Mystery of the Equal Sign

When working with TensorFlow, particularly with datasets, you may encounter behavior that seems puzzling at first glance. A common question revolves around the equal sign and its role in assigning values to variables when utilizing TensorFlow dataset operations. In this post, we'll explore this issue in depth using a coding example to clarify why certain behaviors occur.

The Code Example

Let's start with a brief look at the code that highlights the problem.

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

You might expect the final two printed results (ds22 and ds22s) to contain the same elements, albeit in different orders. However, when running the code, the output reveals a discrepancy that raises several questions:

ds22 returns [2, 6, 8, 0, 7, 3, 9]

ds22s returns [1, 0, 2]

Why is this happening?

The Underlying Issues

To understand this behavior, we need to dissect how TensorFlow dataset operations work, especially the concept of operations versus assignments.

Operations vs. Assignments

Operations Defined:

In TensorFlow, when you declare ds22 = dataset2.skip(7), you're not immediately importing the data from dataset2. Instead, you're defining a new operation that describes how to obtain the data.

This operation is executed only when you iterate over ds22, which means it processes the dataset on demand.

Reiteration:

When you reference ds22s, you are actually making a reference to ds22. Thus, both ds22 and ds22s point to the same underlying operation. At this point, both variables are linked; therefore, any iteration over one will affect the other.

Effect of Shuffling:

The shuffling behavior (reshuffle_each_iteration=True) causes the dataset to generate a different order of elements each time you iterate over it. Since both variables share the same underlying dataset and the shuffling is applied upon each iteration, they may yield different results on separate calls.

Disabling shuffling ensures the same order during subsequent iterations, allowing for consistent results.

Insightful Consideration

When you run your iterator:

Calling print(list(ds22.as_numpy_iterator())) processes all the data that remains after skipping the first 7 entries.

When you later call print(list(ds22s.as_numpy_iterator())), the data will be accessed again but with a reshuffled order because it's referencing an operation that is evaluated anew.

Conclusion

In summary, the behavior you've encountered stems from how operations are defined and executed in TensorFlow datasets. Understanding that commands like ds22 = dataset2.skip(7) do not produce static snapshots of data but rather define operations can clarify many questions about the dataset's behavior.

If you're ever in doubt while working with TensorFlow datasets, remember that you are often defining operations rather than storing fixed values, which can lead to confusion regarding output consistency.

Feel free to reach out for any further clarifications or questions about TensorFlow datasets and how they operate!