Debugging and analyzing neural network training based on audio generated corresponding to gradients during training time.

Doing this project because I have a lot of time on my hands and it seemed like a good idea to pass my time. 😐

Most of the generated samples have been on SGD using variable learning rates.
The training has been done on a four layer NN for 200 steps in one pass using a small batch size(20)
Higher norms are indicated with higher pitch of the sound. In most cases the pitch increases to indicate the increase in gradient over time.

Audio Sample: lr_0.01
Notice the pitch increasing slowly over time indicating slow increase in gradients.

Audio Sample: lr_0.1
Notice how the gradients diverge due to higher learning rate and then converge back in again and again as it jumps great distances on the error curve towards minima.

Audio Sample: lr_1.0
Here you'll see how it diverges the neural network and it doen't converge after that as the gradients explode.

Audio Sample: lr_1.0_bs_256
Warning: this gave me a minor headache.
Notice how the batch size increase affects the gradients as they explode instantly and lead to the annoying low pitch thumping sound caused by gradients becoming "NaN"

Requirements are listed in requirements.txt file. The project requires PyTorch along with Torchvision and PyAudio.
Requirements can be installed as pip install -r requirements.txt

1. model.py: Model architecture file. By default takes batch size as 20. can be modified a little to generate 256 batch size result as well.
2. utils.py: Utility functions for opening audio stream and other things.
3. main.py: Main file to run the code. By default generates the three LR based audios for batch size 20.
   Run the code as python main.py

There is nothing of value here, just did this because it seemed like fun. I don't see anyone debugging their training process by analyzing weights using sound over visualizing weights using graphs.