Neural Architecture Search with Reinforcement LearningNeural Architecture Search with Reinforcement LearningBarret Zoph and Quoc V. Le2016
Paper summaryleopaillier_Objective:_ Design a network that will itself find the best architecture for a given task.
_Dataset:_ [CIFAR10](https://www.cs.toronto.edu/%7Ekriz/cifar.html) and [PTB](https://catalog.ldc.upenn.edu/ldc99t42).
The meta-network (a RNN) generates a string specifying the child network parameters. Such a child network is then trained for 35-50 epochs and its accuracy is used as the reward to train the meta-network with Reinforcement Learning.
The RNN first generates networks with few layers (6) then this number is increased as training progresses.
They develop one architecture for CNN where they predict each layers characteristic plus it's possible skip-connection:
[![screen shot 2017-05-24 at 8 13 01 am](https://cloud.githubusercontent.com/assets/17261080/26389176/d807de42-4058-11e7-942a-8a129558e126.png)](https://cloud.githubusercontent.com/assets/17261080/26389176/d807de42-4058-11e7-942a-8a129558e126.png)
And one specific for LTSM-style:
[![screen shot 2017-05-24 at 8 13 26 am](https://cloud.githubusercontent.com/assets/17261080/26389190/e2bfd506-4058-11e7-9168-62abd040156e.png)](https://cloud.githubusercontent.com/assets/17261080/26389190/e2bfd506-4058-11e7-9168-62abd040156e.png)
## Distributed setting:
Bellow is the distributed setting that they use with parameter servers connected to replicas (GPUs) that trained child networks.
[![screen shot 2017-05-24 at 8 09 05 am](https://cloud.githubusercontent.com/assets/17261080/26389084/5e354456-4058-11e7-83a9-089cb2c115b7.png)](https://cloud.githubusercontent.com/assets/17261080/26389084/5e354456-4058-11e7-83a9-089cb2c115b7.png)
Overall they trained 12800 networks on 800 GPUs but they achieve state of the art results which not human intervention except the vocabulary selection (activation type, type of cells, etc). Next step, transfer learning from one task to another for the meta-network?
First published: 2016/11/05 (2 years ago) Abstract: Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.84, which is
only 0.1 percent worse and 1.2x faster than the current state-of-the-art model.
On the Penn Treebank dataset, our model can compose a novel recurrent cell that
outperforms the widely-used LSTM cell, and other state-of-the-art baselines.
Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is
3.6 perplexity better than the previous state-of-the-art.
### Main Idea:
It basically tunes the hyper-parameters of the neural network architecture using reinforcement learning. The reward signal is taken as evaluation on the validation set. The method is policy gradient as the cost function is non-differentiable.
#### i. Actions:
1. There is controller RNN which predicts some hyper-parameters of the layer conditioned on the previous predictions. This prediction is just a one-hot vector based on the previous hyper-parameter chosen. At the start for the first prediction - this vector is just all zeros.
2. Once the network is generated completely, it is trained for a fixed number of epochs, the reward signal is calculated based on the evaluation on the validation set.
#### ii. Training:
1. Nothing fancy in the reinforcement learning approach simple policy gradients.
2. Baseline is added to reduce the variance.
3. It takes 2-3 weeks to train it over 800 GPUs!!
#### iii. Results:
1. Use it to generate CNNs and LSTM cells. Close to state-of-art results with generated architectures.
### Possible new directions:
1. Use better techniques RL techniques like TRPO, PPO etc.
2. Right now, they generate fixed length architecture. Their reason is for variable length architectures, it is difficult to determine how much time each architecture is trained. Smaller networks are easier to train. Thus, somehow determine training time as function of the learning capacity of the network.
They haven't released the code yet. I tried to simulate it in torch.(https://github.com/abhigenie92/nn_search)