mars-wei/neural-architecture-search

Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).

Neural Architecture Search with Controller RNN

Basic 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

Usage

At a high level : For full training details, please see train.py.

# construct a state space
state_space = StateSpace()

# add states
state_space.add_state(name='kernel', values=[1, 3])
state_space.add_state(name='filters', values=[16, 32, 64])

# create the managers
controller = Controller(tf_session, num_layers, state_space)
manager = NetworkManager(dataset, epochs=max_epochs, batchsize=batchsize)

# For number of trials
  sample_state = ...
  actions = controller.get_actions(sample_state)
  reward = manager.get_reward(actions)
  controller.train()

Implementation details

This is a very limited project.

• Note: The controller eventually overfits to produce a single model always (generally a high performance model, but not always the highest). It seems there os a requirement to have a pool of controllers to avoid this overfitting, which would need distributed training. However, since the exploration rate is so high, there is a large set of very good models that are generated during the training process, and the controller yields a pretty strong model as well.
• It doesnt have support for skip connections via 'anchor points' etc. (though it may not be that hard to implement it as a special state)
• A lot of the details of the Controller were found in slides etc and not in the paper so I had to take many liberties when re-implementing it.
• Learning rate, regularization strength, discount factor, exploration, beta value for EWA accuracy, clipping range etc are all random values (which make somewhat sense to me)
• Single GPU model only. There would need to be a lot of modifications to this for multi GPU training (and I have just 1)

Implementation details were found from:

• http://rll.berkeley.edu/deeprlcoursesp17/docs/quoc_barret.pdf

Result

I tried a toy CNN model with 4 CNN layers with different filter sizes (16, 32, 64) and kernel sizes (1, 3) to maximise score in 10 epochs of training on CIFAR-10.

After 50 steps, it converges to the "state space" of (3x3, 64)-(3x3, 64)-(3x3, 32)-(3x3, 64). Interestingly, this model performs very slightly better than a 4 x (3x3, 64) model, at least in the first 10 epochs.

Requirements

• Keras >= 1.2.1
• Tensorflow-gpu >= 1.2

Acknowledgements

Code heavily inspired by wallarm/nascell-automl