AndreaCossu / Relation-Network-PyTorch

Implementation of Relation Network and Recurrent Relational Network using PyTorch v1.3. Original papers: (RN) https://arxiv.org/abs/1706.01427 (RRN): https://arxiv.org/abs/1711.08028

Relation-Network-PyTorch

Implementation of Relation Network. Original paper: https://arxiv.org/abs/1706.01427

Implementation of Recurrent Relational Network. Original paper: https://arxiv.org/abs/1711.08028

This repository uses PyTorch v1.3 (Python3.7).

Implementation details

This implementation tests the Relation Network model (RN) and the Recurrent Relational Network model (RRN) against the babi dataset, available at https://research.fb.com/downloads/babi/

Weights and Biases

This repository uses Weights and Biases (W&B) to monitor experiments. You can create a free account on W&B (https://www.wandb.com/) or comment out the (few) lines starting with wandb. Without W&B, accuracy and loss plots will still be created and saved locally in the results folder.

Prerequisites

• Run pip install -r requirements.txt
• Create a folder babi
• Download the babi dataset (20 tasks) already divided and preprocessed in train, validation and test at this link: https://drive.google.com/drive/folders/1HkO_w7hbxxWZV4nbGl0Y2w_AuSa4ktub?usp=sharing and put the downloaded folder inside babi.
• Download the word dictionaries at: https://drive.google.com/drive/folders/1Ktd4kL1FJBiY_R_HoqlicjmRemkq8xzC?usp=sharing and put the downloaded folder inside babi.
• Create a folder results to store experiment results (both plots and saved models)

Train and test RN

• Model implementation is inside src/models/RN.py
• Train and test functions are inside task/babi_task/rn/train.py
• The main script is launch_rn_babi.py.
  • Run it with python launch_rn_babi.py experiment_name [options].
  • Options are listed and explained with python launch_rn_babi.py --help.

To reproduce results execute python launch_rn_babi.py test --learning_rate 1e-4 --batch_size 20 --epochs 50 and then check under results/test to see the results. If you want to do the final test on the test set instead of validation set, use --test_on_test option. The final accuracy on test set is (task: accuracy):

• 1: 0.973
• 2: 0.828
• 3: 0.786
• 4: 0.884
• 5: 0.957
• 6: 0.985
• 7: 0.962
• 8: 0.996
• 9: 0.979
• 10: 0.967
• 11: 0.958
• 12: 0.968
• 13: 0.973
• 14: 0.972
• 15: 0.97
• 16: 0.463
• 17: 0.545
• 18: 0.957
• 19: 0.483
• 20: 0.999

Observations

• Batchify babi is essential to training performance, both in terms of convergence time and in terms of final accuracy.
• In order to batchify babi it is necessary to pad supporting facts both on #words and #facts dimensions.
• Relu activation dramatically improves accuracy, but only when using batch size > 1. If batch size == 1 relu prevents learning, while tanh achieves ~74% accuracy on the joint dataset.
• You can find the version with batch size == 1 in the branch no_batch.

Train and test RRN

• Model implementation is inside src/models/RRN.py
• Train and test functions are inside task/babi_task/rrn/train.py
• The main script is launch_rrn_babi.py.
  • Run it with python launch_rrn_babi.py experiment_name [options].
  • Options are listed and explained with python launch_rrn_babi.py --help.

To reproduce results execute python launch_rrn_babi.py test --cuda --epochs 500 --batch_size 512 --weight_decay 1e-5 --learning_rate 2e-4 and then check under results/rrn/test to see the results. If you want to do the final test on the test set instead of validation set, use --test_on_test option. The final accuracy on validation set is (task: accuracy):

• 1: 0.974
• 2: 0.736
• 3: 0.674
• 4: 1.0
• 5: 0.922
• 6: 0.953
• 7: 0.994
• 8: 0.975
• 9: 0.931
• 10: 0.855
• 11: 0.876
• 12: 0.939
• 13: 0.912
• 14: 0.846
• 15: 1.0
• 16: 0.454
• 17: 0.980
• 18: 0.995
• 19: 0.498
• 20: 0.999