Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 

Keras-surgeon

A library for performing network surgery on trained Keras models. Useful for deep neural network pruning.

Keras-surgeon provides simple methods for modifying trained Keras models. The following functionality is currently implemented:

  • delete neurons/channels from layers
  • delete layers
  • insert layers
  • replace layers

Keras-surgeon is compatible with any model architecture. Any number of layers can be modified in a single traversal of the network.

These kinds of modifications are sometimes known as network surgery which inspired the name of this package.

Background

This project was motivated by my interest in deep learning and desire to experiment with some of the pruning methods I have read about in the research literature.

I created this package because I could not find an easy way to prune neurons from Keras models. I hope it will be useful to others.

Install

Keras-Surgeon is installed from PyPI using pip.

pip install kerassurgeon

If you'd like to install the examples' dependencies:

pip install kerassurgeon[examples]

It is compatible with tensorflow.keras and standalone keras.

Usage

The operations module contains simple methods to perform network surgery on a single layer within a model.
Example usage:

from kerassurgeon.operations import delete_layer, insert_layer, delete_channels
# delete layer_1 from a model
model = delete_layer(model, layer_1)
# insert new_layer_1 before layer_2 in a model
model = insert_layer(model, layer_2, new_layer_3)
# delete channels 0, 4 and 67 from layer_2 in model
model = delete_channels(model, layer_2, [0,4,67])

The Surgeon class enables many modifications to be performed in a single operation.
Example usage:

# delete channels 2, 6 and 8 from layer_1 and insert new_layer_1 before 
# layer_2 in a model
from kerassurgeon import Surgeon
surgeon = Surgeon(model)
surgeon.add_job('delete_channels', layer_1, channels=[2, 6, 8])
surgeon.add_job('insert_layer', layer_2, new_layer=new_layer_1)
new_model = surgeon.operate()

The identify module contains methods to identify which channels to prune.

Examples

Examples are in kerassurgeon.examples.
Both examples identify which neurons to prune using the method described in Hu et al. (2016): those which have the highest Average Percentage of Zeros (APoZ).
Neither example is particularly good at demonstrating the benefits of pruning but they show how Keras-surgeon can be used.
I would welcome any good examples from other users.

Pruning Lenet trained on MNIST

lenet_minst is a very simple example showing the effects of deleting channels from a simple Lenet style network trained on MNIST. It demonstrates using the simple methods from kerasurgeon.operations.

Inception V3 fine-tuned on flowers data-set

This example shows how to delete channels from many layers simultaneously using the Surgeon Class.
It is in two parts:
inception_flowers_tune shows how to fine-tune the Inception V3 model on a small flowers data set (based on a combination of Tensorflow tutorial and Keras blog post).
inception_flowers_prune demonstrates deleting channels from many layers simultaneously using the Surgeon Class.

Limitations

Many commonly used layer types are fully supported. Models containing other layer types may cause errors depending on if the unsupported layers are affected by the operation. Some layers downstream of pruned layers are also affected.

Recurrent layers’ sequence length must be defined.
The model’s input shape must be defined.

License

MIT © Ben Whetton