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2 years ago | |
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.. | ||
algorithm | 2 years ago | |
README.md | 2 years ago | |
datasets.py | 2 years ago | |
network_morphism_retrain.py | 2 years ago | |
network_morphism_select.py | 2 years ago | |
network_morphism_train.py | 2 years ago | |
network_morphism_trainer.py | 2 years ago | |
utils.py | 2 years ago |
The implementation of the Network Morphism algorithm is based on
Auto-Keras: An Efficient Neural Architecture Search System
Train stage
python network_morphism_train.py
--trial_id 0
--experiment_dir 'tadl'
--log_path 'tadl/train/0/log'
--data_dir '../data/'
--result_path 'trial_id/result.json'
--log_path 'trial_id/log'
--search_space_path 'experiment_id/search_space.json'
--best_selected_space_path 'experiment_id/best_selected_space.json'
--lr 0.001 --epochs 100 --batch_size 32 --opt 'SGD'
select stage
python network_morphism_select.py
retrain stage
python network_morphism_retrain.py
--data_dir '../data/'
--experiment_dir 'tadl'
--result_path 'trial_id/result.json'
--log_path 'trial_id/log'
--best_selected_space_path 'experiment_id/best_selected_space.json'
--best_checkpoint_dir 'experiment_id/'
--trial_id 0 --batch_size 32 --opt 'SGD' --epochs 100 --lr 0.001
The best model searched achieved 88.1% on CIFAR-10 dataset after 100 trials.
Dependencies:
Python = 3.6.13
pytorch = 1.8.0
torchvision = 0.9.0
scipy = 1.5.2
scikit-learn = 0.24.1
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