tfzip

An experimental compression pipeline for TensorFlow.

Goal

This project aims to implement a TensorFlow version of the Deep Compression pipeline outlined by Han et al.

Initial results:
- Running compression_test.py can generate uncompressed_model and compressed_model protobuf files from a simple MNIST model for comparison.
- Note that there is no difference in the protobuf file sizes until you apply gzip or some other compression tool.
- The provided protobuf files were generated using a LeNet-300-100 model trained for MNIST digit classification.

	Parameters	Parameter Compression	Protobuf Size	Protobuf Size (gzipped)	Protobuf Compression	Accuracy
Uncompressed	~267k		3.2MB	~2.8MB		98.19%
Compressed	~27k	~10x	3.2MB	~490kB	~6x	97.31%

Next steps:
- Improve accuracy preservation and increase compression ratio.
  - Learning both Weights and Connections for Efficient Neural Networks suggests ~12x parameter compression without loss of accuracy can be obtained for this architecture.
- Refactor compression logic so that it can be more easily applied to other networks.