Neural Architecture Search with Reinforcement Learning (1611.01578v2)

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 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. 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 model. The cell can also be transferred to the character LLMing task on PTB and achieves a state-of-the-art perplexity of 1.214.

• The paper pioneers an RL framework using a controller RNN to autonomously design neural architectures that surpass hand-crafted models.
• It leverages policy gradients and distributed training to explore a vast search space, including skip connections and recurrent cell structures.
• Experiments on CIFAR-10 and Penn Treebank validate the approach with a 3.65% test error and improved perplexity, showcasing robust performance.

Neural Architecture Search with Reinforcement Learning: A Summary

"Neural Architecture Search with Reinforcement Learning" by Barret Zoph and Quoc V. Le introduces a pioneering approach for automating the design of artificial neural network (ANN) architectures using Reinforcement Learning (RL).

Introduction and Motivation

The paper starts by placing neural architecture design in context, highlighting its crucial role in achieving state-of-the-art results across various applications such as image recognition and natural language processing. Traditional methods heavily relied on human expertise for architecture design, motivating the need for automated solutions to uncover potentially superior network configurations.

Methodology

The proposed methodology leverages a recurrent neural network (RNN) as a controller to generate architectural descriptions for neural networks. These networks, referred to as "child networks", are trained, and their performance on a validation set is used as a reward signal to update the controller through RL techniques, specifically the REINFORCE algorithm.

Key Components:

1. Controller RNN:
   • Generates sequences of hyperparameters that describe the neural network architecture.
   • Uses softmax classifiers to predict these hyperparameters.
   • Employs an auto-regressive mechanism where each prediction is conditioned on the preceding ones.
2. Training with Reinforcement Learning:
   • Validation accuracy of generated architectures is used as the reward signal.
   • Policy gradient method (REINFORCE) is employed to maximize the expected reward.
   • Baseline function is introduced to reduce the variance of the gradient estimate.
3. Parallelism and Asynchronous Updates:
   • Distributed training framework that uses parameter servers and worker replicas.
   • Facilitates efficient training by allowing simultaneous training of multiple architectures on several GPUs/CPUs.
4. Complexity with Skip Connections:
   • Introduces a method to predict skip connections rather than fixed direct connections between sequential layers.
   • Expands the search space to include modern architectural strategies like residual networks.
5. Recurrent Cell Generation:
   • Extends the method to recurrent architectures.
   • Controller RNN generates a tree structure of operations for recurrent cells.

Experiments and Results

CIFAR-10 Image Classification

The proposed method was applied to the CIFAR-10 dataset for image classification. Key outcomes include:

• Performance Metrics:
  • Achieved a test error rate of 3.65%, outperforming several state-of-the-art human-designed models.
  • Demonstrated architectures with fewer parameters while maintaining competitive performance.
• Architecture Insights:
  • Novel use of rectangular filters and crucial one-step skip connections.
  • Emphasized that the discovered architectures are optimized local optima.

Penn Treebank LLMing

The technique was also tested on the Penn Treebank dataset, focusing on recurrent cell architectures:

• Results:
  • Achieved a test perplexity of 64.0 with a specific architecture, surpassing previous models by nearly 3.6 perplexity on the standard benchmark.
  • Showed the potential for transfer learning, where the discovered cell outperformed the LSTM in character-level LLMing tasks.
• Transfer Learning:
  • Applied the best-found cell to character LLMing on the same dataset, achieving a state-of-the-art perplexity of 1.214 bits per character.
  • Validated the cell’s generality by integrating it into the GNMT framework for translation tasks, yielding a 0.5 BLEU score improvement.

Comparative Analysis and Baselines

The paper provided robust comparison against state-of-the-art methods and baselines:

• Random Search Baseline:
  • Demonstrated that policy gradient optimization consistently outperforms random search across numerous trials.
  • Illustrated through detailed performance graphs.
• Added Complexity Handling:
  • Tested model performance when adding more functions such as sine and max operations to the search space, emphasizing robustness.

Conclusion

The findings validate the practicality and efficacy of using a reinforcement learning framework to automate neural architecture design. By handling complex search spaces and generating architectures that align with or surpass human-crafted designs, this method signifies a significant step towards more intelligent and efficient ANN design practices.

Future Directions

Potential avenues for future research include:

• Extending the framework to other domains and tasks, such as more complex datasets and broader application areas.
• Refining the methodology for even greater computational efficiency and stability.
• Formulating more nuanced reward mechanisms that can capture additional performance metrics beyond validation accuracy.

The release of the code and integration of the discovered cell into TensorFlow emphasizes the reproducibility and applicability of the research, fostering further exploration in the community.