Скачать или смотреть Converting Complex Tensors to JS Arrays in TensorFlow.js

Learn how to seamlessly convert complex tensors to JavaScript arrays in TensorFlow.js, sidestepping common pitfalls and achieving your expected output.
---
This video is based on the question https://stackoverflow.com/q/62651212/ asked by the user 'Sunder' ( https://stackoverflow.com/u/6564811/ ) and on the answer https://stackoverflow.com/a/62653706/ provided by the user 'edkeveked' ( https://stackoverflow.com/u/5069957/ ) 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: Converting Complex Tensors to JS Arrays

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.
---
Converting Complex Tensors to JS Arrays in TensorFlow.js

In the world of machine learning and scientific computing, working with complex numbers is frequently necessary. However, when using TensorFlow.js, converting complex tensors to standard JavaScript arrays can sometimes lead to unexpected results—particularly, the output array may not match your expectations. If you’ve found yourself in a situation where the returned array from your complex tensor operation is twice the size of the original tensor, rest assured you’re not alone. Let's take a closer look at the issue and how to effectively resolve it.

The Problem

When you create complex tensors using TensorFlow.js by combining real and imaginary parts, the data() or dataSync() functions ultimately return a flat array. In this case:

Given Input: Two separate tensors representing real (r = tf.tensor([1, 2, 3])) and imaginary (i = tf.tensor([4, 5, 6])) parts.

Returned Output: A Float32Array(6) [1, 4, 2, 5, 3, 6].

Expected Output: A complex number representation like this: [{ re: 1, im: 4 }, { re: 2, im: 5 }, { re: 3, im: 6 }].

This discrepancy is attributed to the structure of how complex tensors are handled within TensorFlow.js, leading to the confusion.

The Solution

To create a complex array from a complex tensor, we need to reshape our data into the desired format. Here's how to tackle this issue step by step:

Step 1: Create the Real and Imaginary Tensors

Start by defining your real and imaginary tensors:

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

Step 2: Combine into a Complex Tensor

Utilize the tf.complex() function to create a complex tensor:

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

Step 3: Extract Data Synchronously

Next, retrieve the data synchronously, which will give you the flat array representation of the tensor:

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

Step 4: Convert to Desired Format

At this point, you need to format the flat array into an array of objects (each with a re for real and im for imaginary). Use reduce and map to efficiently restructure the data:

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

Breakdown of the Transformation Code:

Reduce Logic: Pairs the real and imaginary parts together.

Map Logic: Converts the pairs into objects with properties re and im for clarity and usability.

Result

Executing this code will yield the expected output:

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

Now, you have successfully transformed your complex tensor into a JavaScript array formatted as intended!

Conclusion

Converting complex tensors to JavaScript arrays in TensorFlow.js can indeed pose a challenge, but understanding the underlying structure of the data can streamline the process significantly. By following the steps outlined above, you can avoid common pitfalls and confidently manipulate complex tensors for your data science and machine learning projects.

If you have any questions or need further clarification, feel free to reach out in the comments below!