BSnet
- Boolean Structured Deep Learning Network
- The network is designed to mimic monotone circuit in Boolean algebra theory
- Tell the LeNet bully to get lost
- No data augmentations, no regularization such as weight decay and dropout
Main Takeaways
- My model has 30000 parameters compared to LeNet 60000 parameters
- Model based on the theory of monotone circuit of Boolean algebra
- Under certain conditions, the training optimization function is convex
- Use fully connected layers without overfitting
- Able to be trained on a laptop without GPU
- Able to achieve 80% classification accuracy after masking 70% of the input image pixels to value 1. A normal deep learning model can only achieve 60%
How to Run
The commands are designed to run on Windows OS. If you are using Linux, adapt the commands accordingly.
Run the command to train a BSnet
python keras_first_network_bsnet.py >> bsnet.txt
Run the command to train a Normal Relu Network
python keras_first_network_normal.py >> normal.txt
Run the command to plot out the accuracies curves
python plot_acc.py
Model
Experiment Results
Links
Discrete Markov Random Field Relaxation
That's it. Have a Nice Day!!!