Awesome

model_compression

Implementation of model compression with three knowledge distilling or teacher student methods [1][2][3]. The basic architecture is teacher-student model.

cifar-10

I used cifar-10 dataset to do this work.

Download cifar-10 dataset

wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

Implementation

In this the work, I use network in network[5] as teacher model, lenet[6] as student model. The teacher model is pre-trained by caffe. And extract the model weight by [4]. Both network-in-network and lenet have little different from original model. In docs, there are two images for the network architecture.

"teacher.npy" is the pre-trained model weights of teacher model.

"student.npy" is the model weights train on lenet, using ground turth label directly.

#Usage In teacher-student.py, there is three methods to train student network. You need to modify the cifar-dataset-path in function read_cifar10

###Basic Usage train by [1]

python teacher-student.py --task train --model savemodel

train by [2]

python teacher-student.py --task train --model savemodel --noisy [--noisy_ratio --noisy_sigma]

train by [3]

python teacher-student.py --task train --model savemodel --KD [--lamda --tau]

**testing** >python teacher-student.py --task test --model trained_model **validation** Also, you can validate your pre-trained teacher model by > python teacher-student.py --task val

This can make sure that your caffe-teacher-model transfer to tensorflow successfully. python teacher-student.py -h for more information

Result

All three methods train 100 epochs, with dropout ratio=0.8, lr=1e-3, decay 0.1 at 80th epoch. In method[2], noisy_ratio=0.5, sigma=0.1. In methos[3], lamda=0.3, tau=0.3.

This table shows the accuracy on testing dataset, test by 100-epoch-model. See more details in result.

method[1]	method[2]	method[3]
71.97%	70.63%	70.96%

The accuarcy of original model which directly learn by ground truth label: teacher model : 78.1% student model : 66.15%

References

[1] Ba, J. and Caruana, R. Do deep nets really need to be deep? In NIPS 2014.

[2] Bharat Bhusan Sau Vineeth N. Balasubramanian, Deep Model Compression: Distilling Knowledge from Noisy Teachers. arXiv 2016.

[3] Hinton, G. E., Vinyals, O., and Dean, J. Distilling the knowledge in a neural network. arXiv 2015.

[4] https://github.com/ethereon/caffe-tensorflow

[5] Network in Network model - https://github.com/aymericdamien/TensorFlow-Examples/

[6] Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE 1998