Welcome to the documentation of the TensorFlow Pruning Toolkit! This toolkit is designed to help users efficiently prune neural networks for better performance and reduced model size.

Quick Start

Here's a brief overview of how to get started with the TensorFlow Pruning Toolkit:

Install TensorFlow Pruning Toolkit

Before you can use the TensorFlow Pruning Toolkit, you need to install it. You can do this by following the instructions in the official installation guide.

Pruning a Model

To prune a model using the TensorFlow Pruning Toolkit, you can follow these steps:

  1. Load your model.
  2. Apply a pruning scheme to the model.
  3. Train the pruned model.

For more detailed instructions, please refer to the pruning guide.

Fine-tuning the Pruned Model

After pruning your model, it's important to fine-tune it to recover any lost accuracy. The toolkit provides various methods for fine-tuning, including:

Resources

Pruning Example

Here's an example of how to prune a layer in a model:

import tensorflow as tf

# Load a model
model = tf.keras.applications.VGG16()

# Prune the first convolutional layer
pruning_params = {
    'pruning_schedule': tf.keras.optimizers.schedules.PolynomialDecay(initial_sparsity=0.0, final_sparsity=0.5, begin_step=0, end_step=100)
}

model.layers[0].apply_pruning(pruning_params)

For more examples and tutorials, please visit our GitHub repository.

Pruning in Action

Here's a visual representation of pruning a neural network:

Neural Network Pruning

We hope this documentation helps you get started with the TensorFlow Pruning Toolkit. If you have any questions or feedback, please reach out to us via our GitHub issues page.