Interpretable Customized Neural Network Pruning by Identifying Critical Data Routing Path
This repository contains：
- The Final Report: Interpretable Customized Neural Network Pruning by Identifying Critical Data Routing Path.pdf
- The Report Latex Source Files: LatexSource
- Our Experiment Codes: SourceCode
We implement our pruning algorithm based on the TensorFlow 1.4.1 with CUDA 8.0. We use CIFAR-100 dataset and VGG-16 network for all the experiments. Codes are available at Github Link
To run our code, you have to download:
-
CIFAR-100 Dataset： Assuming the code is put in directory ".", please download the dataset and save it in the directory "./cifar-100-python".
-
Pretrained VGG-16 Model：Assuming the code is put in directory ".", please download the following three files and save it in the directory "./vggNet".
- https://drive.google.com/open?id=1fDZDf7UpsVCn4CGGvI-Jssm3iFQgpyJw
- https://drive.google.com/open?id=1ZJm8-6HIDOLBWXBt92MQjUKqWRbq_xpz
- https://drive.google.com/open?id=1nXcmco9zrJIkOTQTTa4V3_VbTVajExWI
Credit to BoyuanFeng, Github site: https://github.com/BoyuanFeng
And you need to install following python pachages:
- pickle
- json
- keras
- numpy
- tensorflow
- sklearn
We suggest you to install Anaconda for convenience
Therre are several steps to run the code:
- run.py: this file generate class encodes. You should change classes or set loops to run all classes in this file
- trim_and_test.py: this file trim the model and test the accuracy with pruned model which has not been fine tuned yet. Change target_class_id for models of different classes
- run_finetune.py: this file fine tune the pruned model and test the accuracy with fine tuned models. You should change target_class_id for models of different classes
Notice: we use GPU for training, so you should designate certain GPU for training in these files.