DeepCompression

An implementation of Iterative Pruning, current on mnist only.

Thanks this repository

Usage

Iterative Pruning

cd mnist_iterative_pruning
python iterative_prune.py -1 -2 -3

this would train a convolution model on mnist. Then do pruning on fc layer and retraining for 20 times. Finally fc layers would be transformed to a sparse format and saved.

Performance

we have a pretty good pruning performance, keeping accuracy at 0.987 while pruning 99.77% weights in fc layer.

|weight kept ratio|accuracy| |-----------------|--------| |1 |0.99 | |0.7 |0.991 | |0.49 |0.993 | |0.24 |0.994 | |0.117 |0.993 | |0.057 |0.994 | |0.013 |0.993 | |0.009 |0.992 | |0.0047 |0.99 | |0.0023 |0.987 | |0.0016 |0.889 | |0.0011 |0.886 | |0.00079 |0.677 | |0.00056 |0.409 |

in term of inference time, dense vs sparse: 1.47 vs 0.68