PyTorch implementation of Geoffrey Hinton's Forward Forward Algorithm (https://www.cs.toronto.edu/~hinton/FFA13.pdf)
- Save model
- Save checkpoints
- Add pytorch decorators where needed
- Restart training from checkpoints if exist
- Check GPU usage on device move
- Add RNG environment class
- Add Non-Greedy Trainer
- Change train and "validation" logging for NonGreedy to measure at the end of each pass.
- Current embed labels has an issue where by chance it might assign a "negative" sample with the "correct" label.
- Add classes for Goodness
- Add classes for Metrics
- Comment code
- Add preamble comment section
- Restart step metric with every layer. Currently continues to increment layer by layer.
- Early stopping at layer epoch level
- Add reference to PyTorch and Mohammed's implementation
- Add softmax classifier as easy predict
- Move current classifier predict to hard predict
- Add test config
- Load model from location
- Add test script
- Add W&B logging
- Add GLOM model
- Unsupervised case
- Experimentation (threshold)
- Experimentation (goodness function)
- Experimentation (AE GLOM)
- Improve W&B Logging (Gradients)
- Improve W&B Logging (Example of activations)
- Improve W&B Logging (Gradients)
- Improve W&B Logging (Config)
- Improve W&B Logging (Tags)
- Improve W&B Logging (Groups)
- Improve W&B Logging (Charts config - Log Scale, axis labels etc)