GAN Learning Device Hyperparameter Management for Distributed Training
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Solution Overview
Problem
Current methods for minority shot learning of Generative Adversarial Networks (GANs) in large-scale distributed learning environments are inefficient due to the inability to scale learning rate adjustments with increasing batch sizes, which exceeds dataset sizes, hindering the acceleration of training processes.
Innovation Solution
A learning device and method that utilize different hyperparameters for the discrimination and generation models, where the learning rate for the generation model is adjusted based on the number of GPUs used, allowing for efficient training even when the batch size exceeds the dataset size by setting a second learning rate that is proportional to the number of GPUs, while maintaining a constant first learning rate for the discrimination model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the batch size is increased by adding more GPUs for distributed learning, then the processing capacity and training speed are improved, but the learning rate adjustment becomes ineffective when batch size exceeds dataset size
Solution Approach 1:
The patent applies dynamics by making the learning rate adjustment strategy adaptive to the distributed learning scale. The management unit dynamically determines whether to apply linear scaling rule or cyclic learning rate schedule based on the relationship between batch size and dataset size, allowing the system to adapt its hyperparameter adjustment behavior to different training scenarios and GPU configurations
Solution Approach 2:
The patent changes the learning rate parameter based on distributed learning scale. When batch size exceeds dataset size, the system switches to cyclic learning rate schedule with different initial learning rates for generation and discrimination models, effectively adjusting parameters to maintain training effectiveness in large-scale distributed environments
2Quantity of substance
If the number of GPUs is increased for large-scale distributed learning, then the data processing capacity increases, but the minority shot learning of GAN cannot be accelerated effectively
Solution Approach 1:
The system dynamically adjusts the learning rate strategy based on the distributed learning configuration. The management unit determines whether to use linear scaling rule or cyclic learning rate schedule based on real-time assessment of batch size versus dataset size relationship, enabling effective minority shot learning acceleration across different GPU scales
Solution Approach 2:
The patent segments the learning rate adjustment strategy into two distinct approaches: linear scaling rule for scenarios where batch size is smaller than dataset size, and cyclic learning rate schedule for scenarios where batch size exceeds dataset size. This segmentation allows each strategy to be optimized for its appropriate context, improving overall training efficiency
3Productivity
If the batch size is made sufficiently small compared to dataset size for linear scaling rule, then learning efficiency is improved, but this approach cannot be applied to large-scale distributed learning environments
Solution Approach 1:
The patent creates a universal learning rate adjustment system that handles both small and large batch size scenarios. The management unit selects between linear scaling rule and cyclic learning rate schedule based on the batch size to dataset size ratio, making the system universally applicable to both traditional and large-scale distributed learning environments with different GPU configurations
Data Source
AI summary
Provided is a learning device including a data acquisition unit that acquires a data set including a plurality of training data, a generation unit that includes a generation model that outputs pseudo data, a discrimination unit that includes a discrimination model that discriminates whether the input data is either the training data or the pseudo data according to an input of either the training data or the pseudo data, a management unit that sets a first hyperparameter to be used for updating the discrimination model based on a preset hyperparameter, and a second hyperparameter to be used for updating the generation model, and a learning processing unit that updates the discrimination model using the first hyperparameter and updates the generation model using the second hyperparameter.