CIS 700 Project

This is the repository for course CIS 700 Deeplearning Theorem Proving's term project. We trained a GGNN using task 15 of bAbI datast.

Team members

• Ruipeng LIU ([email protected])
• Jiajing Chen ([email protected])
• Minmin Yang ([email protected])
• Chenbin Pan ([email protected])

Dependencies

• pytorch
• tensorboard
• PyYAML
• numpy
• tqdm

If you want to use gpu to accelerate the training process, then please make sure that cuda is installed prior applying option --device cuda to the execution command.

Usage

To run the program, simply execute the following command:

python main.py

Several options can be applied to this command. The options are list as following:

• --exp_name: Name of the experiment. A directory within Experiment will be created with this name to record the training logs. Default: default.
• --epochs: Number of training epochs. Default: 50.
• --train: Flag for training. Default True
• --data_path: Path for the data. Default: babi_data/processed_1/train/15_graphs.txt
  • Depend on the OS that you are using, this option may need corresponding adjustment.
• --batch_size: Batch size. Default: 15
• --lr: Learning rate. Default: 0.01
• --device: Device used for training model. Default: cuda
  • Again, if this option is set to cuda, please make sure that cuda is installed.
• --opt: Optimizer used in training. Options:SGD, Adam. Default: Adam
• --state_dim: Dimension for states. Default: 4
• --annotation_dim: Annotation dimension. Default: 1
• --edge_type: Types of edges in the graph. Default: 2
• --n_nodes: Default: 8
• --n_step: Number of time that propagation is done. Default: 5
• --attention: If given, the GGNN with attention is used, otherwise the original GGNN is used.

For viewing training curve after training, please execute this command in the project's root directory.

tensorboard --logdir Experiment

Acknowledgement

The model and data used in this repository is based on this repository.
The original bAbI project can be found here.