Скачать или смотреть Resolving the TypeError in Keras Custom Loss Functions: Understanding the reduction_indices Issue

Learn how to fix the `TypeError` related to `reduction_indices` in Keras when defining custom loss functions. Discover the proper usage of Keras operations to avoid errors in your machine learning models.
---
This video is based on the question https://stackoverflow.com/q/62830464/ asked by the user 'ProteinGuy' ( https://stackoverflow.com/u/8437546/ ) and on the answer https://stackoverflow.com/a/62832035/ provided by the user 'Homer' ( https://stackoverflow.com/u/10183365/ ) 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: ERROR IN KERAS CUSTOM LOSS "TypeError: Value passed to parameter 'reduction_indices' has DataType float32 not in list of allowed values: int32, int64"

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 and Resolving the TypeError in Keras Custom Loss Functions

When developing machine learning models with Keras, you may encounter various types of errors, one of which is related to custom loss functions. A common issue arises when defining a custom loss function and using the Keras backend methods incorrectly, which leads to a perplexing TypeError. In this guide, we'll explore a specific error message involving reduction_indices and provide a comprehensive solution.

The Problem: What is the TypeError?

You defined a custom loss function in Keras using the formula:

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

However, when running the model's fitting process, you received the following error:

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

This error indicates that there is an issue with how the prod function is invoked within your custom loss definition.

Examining the Code

Here’s the code snippet of your custom loss function that caused the error:

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

What Went Wrong?

The root of the issue lies in the usage of K.prod(). This function requires one tensor, but you have passed two tensors: y_true and y_pred. The second argument provided is misinterpreted as an axis for reduction rather than a tensor, leading to the float32 type error.

The Solution: Correcting the Custom Loss Function

To fix this issue, we can leverage Keras functions that are intended for element-wise operations. You might want to replace K.prod with either tf.keras.layers.multiply() or tf.keras.layers.dot(), which are designed for such tasks.

Here’s how you can modify your custom loss function correctly:

Updated Custom Loss Function

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

Key Changes Made

Use of K.multiply(): This function performs element-wise multiplication between y_true and y_pred, which gives the intended result without errors.

Summation: The result of the product is then wrapped with K.sum() to ensure we are getting a single loss value.

Conclusion

By understanding the issue at hand and making the necessary adjustments to your custom loss function, you can avoid the TypeError relating to reduction_indices. Always be cautious about the functions you use and ensure they align with the expected input types and structures.

This correction can enhance your model's training process, allowing you to focus on achieving optimal performance without unnecessary roadblocks. Happy coding in Keras!