This is the official repository of EvoPruneDeepTL: An Evolutionary Pruning Model: for Transfer Learning based Deep Neural Networks
The implementation of EvoPruneDeepTL is divided in the following folders:
- EvoDeepTLPruning FC1 FC2: the folder contains the python files for the one layer approaches.
- EvoDeepTLPruning Both: this folders contains the python files for the both layer approach.
- CNN pruning methods: contains the implementation of the compared CNN pruning methods in the paper.
- configs: contains the configuration files for each analyzed dataset in the paper.
- convergence images: it contains the images for the convergence of some used datasets.
To execute the code presented above, it is only required:
Python >= 3.6, Keras >= 2.2.4
Then, given the previous folders and a dataset, the command is the following:
python3 main.py configs/configDataset[dataset].csv configGA[Consecutive].csv numberExecution
where:
- dataset names the dataset to analyze.
- the GA configuration could be the one used for the one layer approach, configGA.csv, or the both layer approach, named configGAConsecutive.csv.
- numberExecution referes to the number of execution that we are carrying out.
The used datasets in this paper can be downloaded from:
- SRSMAS: https://sci2s.ugr.es/CNN-coral-image-classification
- RPS: https://www.tensorflow.org/datasets/catalog/rock_paper_scissors
- LEAVES: https://www.tensorflow.org/datasets/catalog/citrus_leaves
- PAINTING: https://www.kaggle.com/thedownhill/art-images-drawings-painting-sculpture-engraving
- PLANTS: https://github.com/pratikkayal/PlantDoc-Dataset
- CATARACT: https://www.kaggle.com/jr2ngb/cataractdataset
EvoPruneDeepTL is able to optimize sparse layers using a genetic algorithm, giving a neural scheme as it is shown.
The following table shows the average results of EvoPruneDeepTL when the comparison is made against CNN pruning methods.
Moreover, we also show the results of our Feature Selection mechanism against the CNN pruning methods.
We have also made a comparison against other feature extractors to select the fittest one to our data:
We have carried out an analysis of the ability of EvoPruneDeepTL to adapt to relevant classes and robustness.
Relevant classes
The first experiment is the elimination of a class for each dataset to check the importance of that class in the data.
The second experiment aggregates each class to the dataset until it is fully completed.
Robustness
We have used a novel metric called CKA (Centered Kernel Alignment) to check the robustness of the obtained pruned neural networks. This comparison has been done against the closes network (using Hamming distance as selection method) and a fully-connected network. The results shows the robustness of EvoPruneDeepTL as the second column compares similar netwworks and the fourth column shows the results from more different networks:
