Hyperparameter Range Determination for Gradient Descent
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Solution Overview
Problem
Current methods for determining hyperparameters in gradient descent methods, such as grid search and random search, are time-consuming and require improvement for more accurate hyperparameter setting.
Innovation Solution
An information processing apparatus and method that acquire conditions related to the loss function, initial values, and model parameters to determine a suitable range for hyperparameters like learning rate, sample size, and number of parameter updates, using specific constants and target errors to optimize hyperparameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grid search or random search is used to determine hyperparameters, then various patterns can be tried to find the best result, but much time is required for trials of various patterns
Solution Approach 1:
The patent changes the approach from exhaustive search (grid/random search) to analytical determination by transforming the hyperparameter selection problem into a mathematical optimization problem. The system calculates optimal hyperparameter ranges based on problem-specific parameters (loss function conditions, target error, initial value conditions, model parameter dimension) using derived formulas, thereby eliminating time-consuming trial-and-error while maintaining high accuracy.
2Measurement precision
If the technique in Patent Literature 1 is used to adjust learning rate according to gradient information, then learning rate can be optimized considering gradient of loss function, but there is still room for improvement in determining hyperparameter range
Solution Approach 1:
The patent incorporates feedback mechanisms by using gradient information and loss function characteristics to dynamically determine hyperparameter ranges. The system continuously refines hyperparameter selection based on feedback from gradient calculations and loss function behavior, creating an adaptive hyperparameter determination process that improves accuracy while managing complexity through systematic feedback loops.
Solution Approach 2:
The patent performs preliminary analysis of the loss function and problem conditions before finalizing hyperparameter selection. By pre-calculating optimal ranges based on problem-specific parameters and gradient characteristics, the system avoids complex iterative adjustments during the actual learning process, thereby reducing overall complexity while maintaining high precision.
Data Source
AI summary
In order to more appropriately determine a range of a hyperparameter for use in learning by a gradient descent method, an information processing apparatus (1) includes: an acquisition unit (11) that acquires at least one selected from the group consisting of a condition to be satisfied by a loss function, a target error, a condition concerning an initial value of the gradient descent method, and a dimension of a model parameter; and a determination unit (12) that determines a range to be satisfied by at least one hyperparameter selected from the group consisting of a plurality of hyperparameters for use in learning by the gradient descent method, the range being determined in accordance with information which has been acquired by the acquisition unit (11).