Adaptive Learning Rate and Compact Neural Architecture for Faster Training
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Solution Overview
Problem
Deep learning model training processes are lengthy and resource-intensive, requiring expert knowledge and many trials to optimize hyperparameters, and there is no consensus on optimal neural network architectures for applications like text-to-speech systems, leading to inefficiencies and high costs.
Innovation Solution
A compact/slim neural architecture with residual modules and an adaptive learning rate function, such as a piecewise linear learning rate, is used to accelerate training, along with reduced bit precision and larger batch sizes to reduce training time and improve model efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional deep learning training methods are used, then model performance can be achieved, but training time is excessively long and resource consumption is high
Solution Approach 1:
The patent segments the training process into distinct phases with different learning rates (warmup phase, linear decay phase, cosine annealing phase). This segmentation allows each phase to be optimized independently, achieving faster convergence while maintaining model performance. The training process is divided into manageable stages rather than using a single uniform approach throughout.
Solution Approach 2:
The patent implements dynamic learning rate adjustment during training, where the learning rate changes adaptively based on the training phase and model performance. This dynamic approach allows the system to take larger steps when appropriate and smaller steps when needed, significantly reducing training time compared to static learning rate methods while maintaining convergence to optimal performance.
2Reliability
If complex neural network architectures are used to improve performance, then model accuracy increases, but training complexity and resource requirements increase
Solution Approach 1:
The patent changes key training parameters including learning rate schedules, batch sizes, and optimization algorithms to achieve better performance without increasing architectural complexity. By optimizing these parameters, the system obtains improved model performance using standard architectures, avoiding the need for increasingly complex network designs.
Solution Approach 2:
The patent incorporates feedback mechanisms through monitoring model performance metrics during training and adjusting hyperparameters accordingly. This feedback loop allows the system to achieve optimal performance through iterative refinement of training parameters rather than through architectural complexity, maintaining simplicity while improving reliability.
3Reliability
If multiple trials are conducted to optimize hyperparameters, then model performance improves, but time consumption and computational cost increase
Solution Approach 1:
The patent performs preliminary action by establishing systematic hyperparameter optimization strategies and learning rate schedules before training begins. By pre-defining optimization paths and parameter ranges based on theoretical analysis and preliminary experiments, the system reduces the number of trials needed during full training, significantly cutting optimization time while maintaining performance improvement benefits.
Data Source
AI summary
Presented herein are embodiments of a training deep learning models. In one or more embodiments, a compact deep learning model comprises fewer layers, which require fewer floating-point operations (FLOPs). Presented herein are also embodiments of a new learning rate function, which can adaptively change the learning rate between two linear functions. In one or more embodiments, combinations of half-precision floating point format training together with larger batch size in the training process may also be employed to aid the training process.