Model Building Server Automated Hyperparameter Optimization
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Solution Overview
Problem
Current machine learning and deep learning model building processes are inefficient due to the manual adjustment of hyperparameters and the difficulty in evaluating optimization models, leading to time-consuming feature selection and algorithm training in applications like face recognition and autonomous-driving.
Innovation Solution
A model building server performs a two-stage configuration coding and optimization procedure, automatically generating configuration combination codes to select features, model algorithms, and adjust hyperparameters, using genetic or particle swarm optimization algorithms to build an optimization model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of hyperparameters is used in model building, then developers can control model training process, but time consumption increases significantly
Solution Approach 1:
The system performs self-service by automatically selecting features and adjusting hyperparameters through optimization algorithms. The model building server autonomously evaluates multiple feature combinations and hyperparameter settings without requiring manual intervention, thereby reducing time consumption while maintaining operational control through automated decision-making processes.
Solution Approach 2:
The system changes parameters by systematically varying feature combinations and hyperparameter values to find optimal configurations. The optimization algorithm automatically adjusts multiple parameters simultaneously, evaluating their impact on model performance and selecting the best combination without manual intervention.
2Adaptability or versatility
If multiple feature categories and model algorithm categories are provided for selection, then model adaptability improves, but configuration complexity increases
Solution Approach 1:
The system uses feedback mechanisms where the optimization algorithm evaluates model performance for each configuration combination and uses this feedback to guide subsequent selections. The algorithm learns from performance metrics and automatically adjusts feature and algorithm selections to achieve optimal results, managing complexity through iterative improvement rather than exhaustive manual configuration.
Solution Approach 2:
The model building server provides universal functionality by handling multiple feature categories and model algorithm categories through a single automated optimization framework. The system can process different types of data and apply various algorithms without requiring separate manual configuration processes for each case, thereby maintaining adaptability while reducing operational complexity.
3Productivity
If automated optimization is implemented for model building, then time efficiency improves, but system complexity increases
Solution Approach 1:
The optimization process is segmented into distinct stages: feature selection, algorithm selection, and hyperparameter tuning. Each stage is handled by dedicated optimization routines that work sequentially or in parallel, breaking down the complex task of model building into manageable components that can be optimized independently while contributing to overall efficiency.
4Measurement precision
If repeated attempts are made to adjust hyperparameters manually, then model accuracy can be improved, but the process becomes time-consuming
Solution Approach 1:
The system performs preliminary action by pre-evaluating multiple feature combinations and hyperparameter settings before final model training. The optimization algorithm explores the configuration space in advance, identifying promising candidates that are then used for definitive model building, thereby achieving high accuracy without requiring multiple repeated manual attempts.
Data Source
AI summary
A model building server and model building method thereof are provided. The model building server stores a model building program having a configuration combination. The model building server randomly generates a plurality of first configuration combination codes for feature categories, model algorithm categories and hyperparameters to set the configuration combination, and runs the model building program based on a first optimization algorithm to determine a first model. According to at least one determined feature category and at least one determined model algorithm category indicated by the configuration combination code corresponding to the first model, the model building server randomly generates a plurality of second configuration combination codes for features, model algorithms and hyperparameters to set the configuration combination, and runs the model building program based on a second optimization algorithm to determine an optimization model.