How can I preprocess unlabeled data effectively for training generative models

Question

With the help of Python programming, can you tell me How can I preprocess unlabeled data effectively for training generative models?

Answer 1

To preprocess unlabeled data effectively for training generative models, you can follow the following approaches:

• Normalization: Scale pixel values to a standard range (e.g., [0, 1] or [-1, 1]).
• Resizing: Ensure all images are of uniform size.
• Augmentation: Apply transformations like flipping, rotation, and cropping to increase data diversity.
• Batching: Create batches of data for efficient training.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Preprocessing:
  • Resize images to a uniform shape.
  • Normalize pixel values to [0, 1] or [-1, 1].
• Augmentation: Increase data variability with random transformations.
• Efficient Loading: Use tf.data pipeline for shuffling, batching, and prefetching.

Hence, this pipeline ensures consistent and diverse input data for training your generative model.