Neural Network Training Acceleration via Dynamic Learning Rate
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Solution Overview
Problem
Deep learning neural network training is resource-intensive and time-consuming, particularly for small entities and individual researchers, due to large training sets and complex algorithms, making it difficult to achieve high-quality results efficiently.
Innovation Solution
A method for accelerating neural network training that involves acquiring a neural network for a deep learning task, performing iterative training using a training data set, and dynamically updating learning rate and momentum based on a preset function and the number of iterations, while incorporating data augmentation techniques such as data cutout and mixup, to reduce resource usage and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural network training methods are used with large training sets and complex algorithms, then model accuracy can be improved, but training time and computational cost increase significantly
Solution Approach 1:
The patent applies dynamics by making the learning rate and momentum parameters variable rather than fixed. The learning rate function dynamically adjusts the learning rate based on the current iteration number, and the momentum function similarly adapts momentum values during training. This dynamic parameter adjustment allows the training process to efficiently navigate the loss landscape, achieving high accuracy without requiring excessively long training times
Solution Approach 2:
The patent implements parameter changes by introducing specific functional forms for learning rate and momentum that vary with iteration count. The learning rate function typically decreases over time to allow larger steps early in training and finer adjustments later, while the momentum function can increase or adjust to help escape local minima. These parameter transformations enable faster convergence to high-accuracy solutions
2Measurement precision
If traditional neural network training methods are used with large training sets and complex algorithms, then model accuracy can be improved, but computational cost increases significantly
Solution Approach 1:
The dynamic parameter adjustment reduces computational cost by optimizing the training trajectory. By adapting learning rates and momentum values during training, the method achieves faster convergence, requiring fewer iteration steps to reach high accuracy. This reduces the total number of forward and backward passes through the network, thereby lowering overall computational cost and energy consumption
Solution Approach 2:
The parameter transformations in the learning rate and momentum functions enable more efficient use of computational resources. The adaptive parameters help the optimization process converge faster by taking appropriate step sizes at different training stages, reducing the total computational work required to achieve high model accuracy compared to fixed parameter methods
3Ease of operation
If fixed learning rate and momentum are used during training, then the training process is simpler, but convergence speed and accuracy are limited
Solution Approach 1:
The patent resolves this contradiction by implementing dynamics in a computationally efficient manner. The learning rate and momentum are adjusted using simple functions of the iteration number rather than complex adaptive algorithms. This maintains ease of operation while significantly improving convergence speed and final accuracy, as the dynamic parameters help navigate the loss landscape more effectively throughout training
Data Source
AI summary
A method for information processing for accelerating neural network training. The method includes: acquiring a neural network corresponding to a deep learning task; and performing iterations of iterative training on the neural network based on a training data set. The training data set includes task data corresponding to the deep learning task. The iterative training includes: processing the task data in the training data set using a current neural network, and determining, based on a processing result of the neural network on the task data in a current iterative training, prediction loss of the current iterative training; determining a learning rate and a momentum in the current iterative training; and updating weight parameters of the current neural network by gradient descent based on a preset weight decay, and the learning rate, the momentum, and the prediction loss in the current iterative training. This method achieves efficient and low-cost deep learning-based neural network training.