https://github.com/titu1994/neural-architecture-search

Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).
https://github.com/titu1994/neural-architecture-search

keras neural-architecture-search tensorflow

Last synced: 9 days ago
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Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).

• Host: GitHub
• URL: https://github.com/titu1994/neural-architecture-search
• Owner: titu1994
• License: mit
• Created: 2017-12-30T15:25:33.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-07-25T21:52:08.000Z (over 3 years ago)
• Last Synced: 2024-10-13T14:17:04.006Z (22 days ago)
• Topics: keras, neural-architecture-search, tensorflow
• Language: Python
• Homepage:
• Size: 105 KB
• Stars: 430
• Watchers: 16
• Forks: 111
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Neural Architecture Search with Controller RNN

Basic implementation of Controller RNN from [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578) and [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012).

- 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`.

```python

# 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](https://github.com/wallarm/nascell-automl)