机器学习：9.3 NAS algorithms

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Neural Architecture Search (NAS)

• A neural network has different types of hyperparameters:

  • Topological structure: resnet-ish, mobilenet-ish, #layers

  • Individual layers: kernel_size, #channels in convolutional layer,

    hidden_outputs in dense/recurrent layers

• NAS automates the design of neural network

  • How to specify the search space of NN

  • How to explore the search space

  • Performance estimation

NAS with Reinforcement Learning

• Zoph & Le 2017

  • A RL-based controller (REINFORCE) for proposing architecture.

  • RNN controller outputs a sequence of tokens to config the model architecture.

  • Reward is the accuracy of a sampled model at convergence

• Naive approach is expensive and sample in�efficient (~2000 GPU days). To speed up NAS:

  • Estimate performance

  • Parameter sharing (e.g. EAS, ENAS)

The One-shot Approach

• Combines the learning of architecture and model params

• Construct and train a single model presents a wide variety of
  architectures

• Evaluate candidate architectures

  • Only care about the candidate ranking

  • Use a proxy metric: the accuracy after a few epochs

• Re-train the most promising candidate from scratch

Differentiable Architecture Search

• Relax the categorical choice to a softmax over possible operations:
• Multiple candidates for each layer
• Output of -th candidate at layer is
• Learn mixing weights . The input for -the layer is with
• Choose candidate
• Jointly learn and network parameters
• A more sophisticated version (DARTS) achieves SOTA and reduces the search time to ~3 GPU days

Scaling CNNs

• A CNN can be scaled by 3 ways:

  • Deeper: more layers

  • Wider: more output channels

  • Larger inputs: increase input image resolutions

• EfficientNet proposes a compound scaling

  • Scale depth by , width by , resolution by

  • so increase FLOP by 2x if

  • Tune

Research directions

• Explainability of NAS result

• Search architecture to fit into edge devices

  • Edge devices are more and more powerful, data privacy concerns

  • But they are very diverse (CPU/GPU/DSP, 100x performance difference) and have power constraints

  • Minimize both model loss and hardware latency

• E.g. minimize

• To what extend can we automates the entire ML workflow?

Summary

• NAS searches a NN architecture for a customizable goal

  • Maximize accuracy or meet latency constraints on particular hardware

• NAS is practical to use now:

  • Compound depth, width, resolution scaling

  • Differentiable one-hot neural network