Neural Network Hyperparameter Optimization via Design of Experiments
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Solution Overview
Problem
Training neural networks is time-consuming and computationally expensive due to the iterative process of adjusting hyperparameters, often requiring repeated trials to achieve an accurate model, especially in complex systems like autonomous driving and large data centers.
Innovation Solution
A systematic approach using Design of Experiments (DOE) methods, specifically factorial and fractional factorial DOE approaches, to establish cause-and-effect relationships between hyperparameters and model accuracy, reducing the number of tests needed by identifying optimal permutations of hyperparameters through a computing system that includes a neural network trainer, training data segmenter, hyperparameter identifier, matrix constructor, training controller, and result comparator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iterative hyperparameter adjustment is used for training neural networks, then model accuracy can be improved through repeated trials, but training time and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by using Design of Experiments (DOE) methodology to pre-identify optimal hyperparameter permutations before full-scale training. The system performs preliminary experiments on a subset of training data to determine which hyperparameter combinations yield the best results, then applies these pre-identified optimal settings to the complete training process. This preliminary identification step avoids the need for repeated iterative trials during full training, significantly reducing training time while maintaining model accuracy.
2Measurement precision
If multiple permutations of hyperparameters are tested to find optimal settings, then model accuracy improves, but the number of tests and computational resources required increase
Solution Approach 1:
The patent applies partial action by testing hyperparameter permutations on a partial subset of training data rather than the complete dataset. The system divides training data into subsets and performs hyperparameter optimization experiments on these smaller portions first. This allows multiple hyperparameter permutations to be tested with reduced computational cost per test, enabling broader exploration of hyperparameter space while maintaining overall training efficiency.
3Measurement precision
If comprehensive hyperparameter optimization is performed, then model accuracy increases, but computational expense and resource consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the hyperparameter optimization process into distinct segments or phases. The system segments the training data into multiple subsets and performs hyperparameter optimization on each segment separately using DOE methodology. This segmented approach allows computational resources to be distributed across multiple smaller optimization tasks rather than one large exhaustive search, reducing peak computational cost while achieving comprehensive hyperparameter optimization through aggregated results.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture for training a neural network are disclosed. An example apparatus includes a training data segmenter to generate a partial set of labeled training data from a set of labeled training data. A matrix constructor is to create a design of experiments matrix identifying permutations of hyperparameters to be tested. A training controller is to cause a neural network trainer to train a neural network using a plurality of the permutations of hyperparameters in the design of experiments matrix and the partial set of labeled training data, and access results of the training corresponding of each of the permutations of hyperparameters. A result comparator is to select a permutation of hyperparameters based on the results, the training controller to instruct the neural network trainer to train the neural network using the selected permutation of hyperparameters and the labeled training data.