This is an implementation of Tucker and CP decomposition of convolutional layers. A blog post about this can be found here.
It depends on TensorLy for performing tensor decompositions.
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Train a model based on fine tuning VGG16:
python main.py --train. -
There should be a dataset with two categories. One directory for each category. Training data should go into a directory called 'train'. Testing data should go into a directory called 'test'. This can be controlled with the flags --train_path and --test_path.
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I used the Kaggle Cats/Dogs dataset.
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The model is then saved into a file called "model".
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Perform a decomposition:
python main.py --decomposeThis saves the new model into "decomposed_model". It uses the Tucker decomposition by default. To use CP decomposition, pass --cp. -
Fine tune the decomposed model:
python main.py --fine_tune
- CP Decomposition for convolutional layers is described here: https://arxiv.org/abs/1412.6553
- Tucker Decomposition for convolutional layers is described here: https://arxiv.org/abs/1511.06530
- VBMF for rank selection is described here: http://www.jmlr.org/papers/volume14/nakajima13a/nakajima13a.pdf
- VBMF code was taken from here: https://github.com/CasvandenBogaard/VBMF
- Tensorly: https://github.com/tensorly/tensorly