This guide provides an overview of the pruning process in TensorFlow Lite, which is a lightweight solution for mobile and embedded devices. Pruning helps reduce the model size and improve inference speed without significantly compromising accuracy.

Pruning Basics

Pruning is the process of removing unnecessary weights from a neural network. By removing these weights, the model becomes smaller and faster. There are two main types of pruning:

  • Structured Pruning: Prunes entire filters or channels at a time, which can lead to better performance but requires more computation.
  • Unstructured Pruning: Prunes individual weights, which is more flexible but can be less effective.

Pruning Steps

  1. Select Pruning Rate: Determine the percentage of weights to be pruned.
  2. Apply Pruning: Remove the selected weights from the model.
  3. Fine-tune: Train the pruned model to recover any lost accuracy.
  4. Quantization: Apply quantization to further reduce the model size and improve inference speed.

Pruning Techniques

Here are some common pruning techniques:

  • Lottery Ticket Hypothesis: Prunes weights randomly and then trains the model until it reaches a certain accuracy threshold.
  • Iterative Pruning: Gradually prunes weights while monitoring the model's performance.
  • Pruning Based on Activation: Prunes weights based on their contribution to the activation of the neurons.

Resources

For more information on TensorFlow Lite pruning, check out the following resources:

Pruning Example