Implementation of N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
L12.1 ppo network compression.ipynb- PPO-Clip with Bernoulli actions to perform network compressionL12.2 ppo cartpole.ipynb- PPO-Clip to playCartPole-v1L12.3 framework comparison.ipynb- PPO-Clip from @seungeunrho/minimalRLL12.4 multi-armed bandits.ipynb- Multi-armed bandits from @cstorm125/michael
With all the hardware accelerations available to us today, many researchers are resorting to larger and larger models to solve their problems. However, larger models also mean more memory usage, longer training and inference time, and more limitations in productionization (for instance, AWS Lambda only allows 512MB in temporary storage).
Techniques like knowledge distillation and pruning have been used to reduce the parameters of neural networks. Here we will adapt a layer removal technique from the paper N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning (See codes here) to compress a VGG-like network to achieve 5x compression and comparable accuracy on Fashion-MNIST dataset.
