Skip to content

vadim-v-lebedev/cp-decomposition

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

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

This is an implemetation of the method from our paper Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition https://arxiv.org/pdf/1412.6553.pdf MNIST example, based on MNIST example from caffe, is provided.

Requrements: caffe, python with numpy and scikit-tensor

How to make it work:

  1. Set paths to your caffe installation in paths.py
  2. In lenet/lenet.prototxt, edit "source" params of input layers, or copy mnist_train_lmbd and mnist_test_lmdb from caffe/examples/mnist here. LeNet needs input data!
  3. run lenet/main.py, for example like this python lenet/main.py 5 conv2. First parameter of this script is the number of components R, and the second is layer name. Biggger R leads to more accurate, but slower models. The script will produce model lenet_accelerated.prototxt and weights file lenet_accelerated.caffemodel
  4. Now you can evaluate accelerated model $CAFFE_ROOT/build/tools/caffe time --model lenet_accelerated.prototxt $CAFFE_ROOT/build/tools/caffe test --model lenet_accelerated.prototxt -weights lenet_accelerated.caffemodel
  5. As shown in the paper, finetuning of accelerated model can improve accuracy

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages