CLI Hyperparameter Tuning via Parameter Description Data
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Solution Overview
Problem
Existing hyperparameter tuning methods for machine learning models, such as deep learning models, require manual adjustments or use inconvenient hyperparameter configuration programs, necessitating direct code writing, which is inefficient and language-dependent.
Innovation Solution
A hyperparameter configuration device that sets hyperparameter values based on parameter description data input through a command-line interface, allowing users to search for suitable hyperparameters without writing program code, and employs methods like Bayesian optimization and early stopping to optimize hyperparameters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of hyperparameters is used, then flexibility and control are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs automated hyperparameter tuning by having the computer automatically search for optimal hyperparameter values based on objective functions and trial results, eliminating the need for manual user intervention in the hyperparameter adjustment process
Solution Approach 2:
The system automatically changes hyperparameter values through systematic search methods (grid search, random search, Bayesian optimization) to find optimal configurations, transforming manual parameter adjustment into an automated computational process
2Extent of automation
If conventional hyperparameter configuration programs are used, then hyperparameter setting capability is improved, but ease of operation deteriorates due to requiring direct code writing
Solution Approach 1:
The system introduces an intermediary layer between the user and the hyperparameter configuration process, where users simply specify the objective function and the system automatically handles the complex search and configuration tasks through intermediate computational steps
Solution Approach 2:
The system makes the hyperparameter configuration process self-service by automatically performing the entire workflow from hyperparameter search to model training without requiring users to write configuration code, making the process as simple as specifying the objective
3Measurement precision
If exhaustive hyperparameter search is performed, then optimization precision is improved, but computational cost and time consumption increase
Solution Approach 1:
The system performs partial search by evaluating only the most promising hyperparameter configurations identified through objective function guidance and early stopping criteria, rather than exhaustively testing all possible combinations, thus achieving good optimization results with reduced computational effort
Solution Approach 2:
The system uses feedback from trial results and objective function evaluations to guide the hyperparameter search process, continuously refining the search direction based on performance metrics to efficiently converge on optimal hyperparameters without exhaustive testing
Data Source
AI summary
A hyperparameter configuration device includes at least one memory, and at least one processor configured to acquire a program execution instruction including parameter description data, the program execution instruction being written through a command-line interface, set a value of a hyperparameter of a program to be trialed, based on the parameter description data, acquire a result of a trial of the program, the trial of the program being executed with the value of the hyperparameter, and set a next value of the hyperparameter of the program, based on the result of the trial.