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.
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.
I used the Kaggle Cats/Dogs dataset.
The model is then saved into a file called "model".
Perform a decomposition:
python main.py --decompose
This 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