pytorch-ewc
Unofficial PyTorch implementation of DeepMind's paper Overcoming Catastrophic Forgetting, PNAS 2017.
Results
Continual Learning without EWC (left) and with EWC (right).
Installation
$ git clone https://github.com/kuc2477/pytorch-ewc && cd pytorch-ewc
$ pip install -r requirements.txt
CLI
Implementation CLI is provided by main.py
Usage
$ ./main.py --help
$ usage: EWC PyTorch Implementation [-h] [--hidden-size HIDDEN_SIZE]
[--hidden-layer-num HIDDEN_LAYER_NUM]
[--hidden-dropout-prob HIDDEN_DROPOUT_PROB]
[--input-dropout-prob INPUT_DROPOUT_PROB]
[--task-number TASK_NUMBER]
[--epochs-per-task EPOCHS_PER_TASK]
[--lamda LAMDA] [--lr LR]
[--weight-decay WEIGHT_DECAY]
[--batch-size BATCH_SIZE]
[--test-size TEST_SIZE]
[--fisher-estimation-sample-size FISHER_ESTIMATION_SAMPLE_SIZE]
[--random-seed RANDOM_SEED] [--no-gpus]
[--eval-log-interval EVAL_LOG_INTERVAL]
[--loss-log-interval LOSS_LOG_INTERVAL]
[--consolidate]
optional arguments:
-h, --help show this help message and exit
--hidden-size HIDDEN_SIZE
--hidden-layer-num HIDDEN_LAYER_NUM
--hidden-dropout-prob HIDDEN_DROPOUT_PROB
--input-dropout-prob INPUT_DROPOUT_PROB
--task-number TASK_NUMBER
--epochs-per-task EPOCHS_PER_TASK
--lamda LAMDA
--lr LR
--weight-decay WEIGHT_DECAY
--batch-size BATCH_SIZE
--test-size TEST_SIZE
--fisher-estimation-sample-size FISHER_ESTIMATION_SAMPLE_SIZE
--random-seed RANDOM_SEED
--no-gpus
--eval-log-interval EVAL_LOG_INTERVAL
--loss-log-interval LOSS_LOG_INTERVAL
--consolidate
Train
$ python -m visdom.server &
$ ./main.py # Train the network without consolidation.
$ ./main.py --consolidate # Train the network with consolidation.
Update Logs
- 2019.06.29
- Fixed a critical bug within
model.estimate_fisher()
: Squared gradients of log-likelihood w.r.t. each layer were mean-reduced over all the dimensions. Now it correctly estimates the Fisher matrix by averaging only over the batch dimension
- Fixed a critical bug within
- 2019.03.22
- Fixed a critical bug within
model.estimate_fisher()
: Fisher matrix were being estimated with squared expectation of gradient of log-likelihoods. Now it estimates the Fisher matrix with the expectation of squared gradient of log-likelihood. - Changed the default optimizer from Adam to SGD
- Migrated the project to PyTorch 1.0.1 and visdom 0.1.8.8
- Fixed a critical bug within
Reference
Author
Ha Junsoo / @kuc2477 / MIT License