NeSy-without-Shortcuts

Code for the paper "Learning with Logical Constraints but without Shortcut Satisfaction"

Requirements

numpy
pytorch

Usage

For each task, run the following command.

python logic_training.py --num_labeled 100 --constraint True --trun True --exp_name logic_trun \

​ --net_type densenet100 --adam_lr 0.01 --constraint_weight 1.0

where the parameter "trun" is set to True to enable the truncation of Gaussian distribution.

To reproduce the experimental results, Run the command sh run.sh

Related work

• We also provide baseline methods in MNIST task, one can refer to /mnist_exp/baselines for more details.

• Our another work ("Softened Symbol Grounding for Neuro-symbolic Systems") can also avoid the shortcuts, but do not need to additionally define the dual variable.