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Neural Architecture Search

Utilization of a Controller using Reinforcement Learning, Sequentual Model Based Optimization to train via surrogate losses, or using XGBoost Trees to reduce the search space.

Neural Architecture Search

Basic and limited (1 GPU) implementation of Controller RNN from Neural Architecture Search with Reinforcement Learning and Learning Transferable Architectures for Scalable Image Recognition.

  • Uses Keras to define and train children / generated networks, which are defined in Tensorflow by the Controller RNN.
  • Define a state space by using StateSpace, a manager which adds states and handles communication between the Controller RNN and the user.
  • Controller manages the training and evaluation of the Controller RNN
  • NetworkManager handles the training and reward computation of a Keras model

Progressive Neural Architecture Search

Basic and limited (1 GPU) implementation of Encoder RNN from Progressive Neural Architecture Search.

  • Uses Keras to define and train children / generated networks, which are found via sequential model-based optimization in Tensorflow, ranked by the Encoder RNN.
  • Define a state space by using StateSpace, a manager which maintains input states and handles communication between the Encoder RNN and the user.
  • Encoder manages the training and evaluation of the Encoder RNN
  • NetworkManager handles the training and reward computation of the children Keras model

Sequentual Halving and Classification

PySHAC is a python library to use the Sequential Halving and Classification algorithm from the paper Parallel Architecture and Hyperparameter Search via Successive Halving and Classification with ease.

Note : This library is not affiliated with Google.

Stable build documentation can be found at PySHAC Documentation.

It contains a User Guide, as well as explanation of the different engines that can be used with PySHAC.