ajayn1997/Neural-Architecture-Search-using-Reinforcement-Learning

An implementation of neural architecture search using the REINFORCE algorithm. we use a re-current network to generate the model descriptions of neural networks and trainthis RNN with reinforcement learning to maximize the expected accuracy of thegenerated architectures on a validation set. This algorithm is tested on the CIFAR-10 dataset. The project is inspired from the work presented in the paper "NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING" by Barret et al from Google Brain.

Python