How can you preprocess image data using tf data pipelines for generative tasks

Question

Can you tell me how you can preprocess image data using tf.data pipelines for generative tasks? Use code snippets if possible.

Answer 1

You can preprocess image data for generative tasks using TensorFlow's tf.data pipelines by loading, normalizing, and batching the data.

Here is the code snippet to which you can refer:

In the above code, we are using the following:

• Load Data: The MNIST dataset is loaded and contains raw grayscale images.
• Preprocessing: Images are resized, normalized to [0, 1], and reshaped to include a channel dimension.
• Pipeline Creation: A tf.data.Dataset is created, shuffled, batched, and prefetched for efficient data loading.
• Usage: The pipeline provides preprocessed image batches ready for training generative models.

Hence, this prepares a dataset for efficient training with resizing, normalization, batching, and prefetching.