Скачать или смотреть Resolve nan Loss in TensorFlow Models When Using pandas.pct_change()

Discover how to handle `nan` losses in TensorFlow models resulting from using pandas' `pct_change()`. Learn to clean your data efficiently!
---
This video is based on the question https://stackoverflow.com/q/65569919/ asked by the user 'Tim' ( https://stackoverflow.com/u/3258980/ ) and on the answer https://stackoverflow.com/a/65571906/ provided by the user 'Tim' ( https://stackoverflow.com/u/3258980/ ) 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: Using pandas.pct_change() on dataset results in 'nan' loss in tensorflow model

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 nan Loss in TensorFlow when Using pandas.pct_change()

Many data enthusiasts and developers working with machine learning face unique challenges when transforming their data for model training. One such issue arises when using the pandas.pct_change() function, which may inadvertently lead to nan (Not a Number) values in TensorFlow models. If you've encountered nan losses during training, you're not alone. Today, we will demystify this problem and help you understand how to effectively resolve it.

The Problem: Encountering nan Losses

Let's say you have a pandas DataFrame structured like this:

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

This will output:

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

Now, when you convert this DataFrame to represent percentage changes:

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

You may notice the following output:

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

Here, the first row contains nan values, and subsequent rows report inf (infinity). This is where the problem lies: TensorFlow models cannot handle nan or inf values effectively during training, leading to persistent nan losses.

How to Address the Issue

Step 1: Understand the Impact of nan and inf Values

TensorFlow requires clean, finite numbers for training. Introducing nan or inf leads to undefined behaviors during backpropagation, causing the loss to remain undefined (nan).

Step 2: Clean Your Dataset

Although it may seem obvious, ensuring that your dataset is devoid of nan or inf values is vital before running the model. Here’s how you can clean your DataFrame:

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

You should see a cleaned output without nan or inf values:

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

Step 3: Training the Model

When you invoke the model.fit() function, ensure that your input data contains no nan or inf values. If you train your model like this:

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

but the training still results in nan losses, it may be because the model has been previously trained on corrupted data.

Key Recommendations:

Only train the model when you are confident the input data is clean. This involves checking for any nan or inf values before the training process begins.

Constantly validate data integrity after any transformation, like using .pct_change().

Conclusion

In conclusion, while the pandas.pct_change() function can play a pivotal role in preparing your dataset, it’s essential to be vigilant about data cleanliness when training models with TensorFlow. Always check for nan and inf values before modeling to avoid frustrating nan losses. With a conscious approach to data integrity, you'll find that your model training can proceed smoothly towards more meaningful outcomes.

It’s simplified, yet an often overlooked step that can make all the difference in your modeling efforts! Happy modeling!