https://github.com/DwangoMediaVillage/keras_compressor

Model Compression CLI Tool for Keras.
https://github.com/DwangoMediaVillage/keras_compressor

deep-learning keras machine-learning model-compression

Last synced: 3 months ago
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Model Compression CLI Tool for Keras.

• Host: GitHub
• URL: https://github.com/DwangoMediaVillage/keras_compressor
• Owner: DwangoMediaVillage
• Created: 2017-05-19T10:42:04.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-05-09T17:35:00.000Z (about 5 years ago)
• Last Synced: 2024-01-18T08:44:23.379Z (5 months ago)
• Topics: deep-learning, keras, machine-learning, model-compression
• Language: Python
• Homepage: https://nico-opendata.jp/ja/casestudy/model_compression/index.html
• Size: 19.5 KB
• Stars: 157
• Watchers: 11
• Forks: 39
• Open Issues: 11
• Metadata Files:
  • Readme: README.md

Lists

• awesome-stars - DwangoMediaVillage/keras_compressor - Model Compression CLI Tool for Keras. (Python)
• awesome-model-compression - Keras compressor - compression using low-rank approximations, SVD for matrices, Tucker for tensors. (PROJECTS / 2023)

README

        
# keras_compressor

Model compression CLI tool for [keras](https://github.com/fchollet/keras).

# How to use it

## Requirements

- Python 3.5, 3.6

- Keras

    - We tested on Keras 2.0.3 (TensorFlow backend)

## Install

```

$ git clone ${this repository}

$ cd ./keras_compressor

$ pip install .

```

## Compress

Simple example:

```

$ keras-compressor.py model.h5 compressed.h5

```

With accuracy parameter `error`:

```

$ keras-compressor.py --error 0.001 model.h5 compressed.h5

```

## Help

```

$ keras-compressor.py --help                                                                               [impl_keras_compressor:keras_compressor]

Using TensorFlow backend.

usage: keras-compressor.py [-h] [--error 0.1]

                           [--log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG}]

                           model.h5 compressed.h5

compress keras model

positional arguments:

  model.h5              target model, whose loss is specified by

                        `model.compile()`.

  compressed.h5         compressed model path

optional arguments:

  -h, --help            show this help message and exit

  --error 0.1           layer-wise acceptable error. If this value is larger,

                        compressed model will be less accurate and achieve

                        better compression rate. Default: 0.1

  --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG}

                        log level. Default: INFO

```

# How compress it

- low-rank approximation

  - with SVD (matrix)

  - with Tucker (tensor)

# Examples

In example directory, you will find model compression of VGG-like models using MNIST and CIFAR10 dataset.

```console

$ cd ./keras_compressor/example/mnist/

$ python train.py

-> outputs non-compressed model `model_raw.h5`

$ python compress.py

-> outputs compressed model `model_compressed.h5` from `model_raw.h5`

$ python finetune.py

-> outputs finetuned and compressed model `model_finetuned.h5` from `model_compressed.h5`

$ python evaluate.py model_raw.h5

$ python evaluate.py model_compressed.h5

$ python evaluate.py model_finetuned.h5

-> output test accuracy and the number of model parameters

```