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Learn how to perform `data augmentation` using TensorFlow-Keras to rotate images during training epochs for improved model accuracy.
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Data Augmentation using TensorFlow-Keras API: A Comprehensive Guide

Data augmentation is a powerful technique in machine learning, specifically in the realm of computer vision. It involves enhancing your training dataset with transformed versions of your data, helping to prevent overfitting and improve the generalization of your model. In this guide, we will explore how to rotate images during training epochs using the TensorFlow-Keras API.

The Problem

Imagine you have a model that relies on image data, and during training, you're interested in applying various transformations to those images to improve the model’s robustness. In the provided code sample, images are set to be rotated by 90 degrees at the end of each epoch. However, the challenge arises on how to integrate this rotation more effectively during each epoch instead of waiting until the next epoch.

The Desired Output

You want the rotations to occur in a structured manner for each epoch, looking something like this:

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

The Solution

Instead of creating a CustomCallback that changes the rotation angle after each epoch, we can modify the data loading function to apply the rotation during the training process. This involves augmenting the images with a probability. In essence, when fetching an image, we decide randomly whether to apply a rotation or not.

Step-by-Step Implementation

Modify the _getitem_ Method in Data Iterator

You'll need to adjust the method that retrieves images to include randomness in the augmentation process. Below is the modified code snippet to include rotation based on probability:

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

Understanding the Code Changes

Probability Check: We generate a random number between 0 and 1. If this number is greater than 0.5, we apply the rotation; otherwise, we do not.

Flexibility: This method allows for a more dynamic augmentation process, providing your model with diverse inputs every time it processes a batch of images.

Training the Model

After implementing these changes, you can continue to train your model as before. Each epoch will now feature different rotations of the images, enhancing the robustness of your model significantly.

Conclusion

By leveraging data augmentation techniques like probabilistic rotation, you can significantly improve the performance of your machine learning models when working with image data. This not only helps in reducing overfitting but also ensures that your model learns to recognize objects from various orientations and perspectives.

If you have any questions or need further clarification on any of the steps, feel free to ask. Happy coding!