Scaling-Invariance Classification Training for Hyperparameter Search
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Solution Overview
Problem
The current setting of hyperparameters for neural network training in classification models is largely empirical, leading to inefficient use of computing resources and poor training effects, often requiring manual adjustment and extensive trial-and-error.
Innovation Solution
Implementing a scaling invariance linear layer in classification models to maintain a fixed modulus length of weight parameters, allowing the equivalent replacement of learning rate and weight decay hyperparameters, thereby reducing the search space and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hyperparameter setting depends on empirical data and manual adjustment, then model training can be performed, but computing resources are consumed excessively and training efficiency is low
Solution Approach 1:
The patent changes the parameter representation from traditional hyperparameters (learning rate, weight decay) to modulus length constraints. By fixing the modulus length of weight parameters, the training process no longer requires extensive hyperparameter tuning, directly improving training efficiency while reducing computing resource consumption.
Solution Approach 2:
The patent extracts the hyperparameter tuning problem from the traditional training process by introducing a scaling invariance linear layer that automatically maintains fixed modulus length of weight parameters. This extraction eliminates the need for manual hyperparameter adjustment and reduces dependency on empirical data.
2Ease of operation
If traditional classification layers are used, then training can proceed, but the modulus length of weight parameters changes during training requiring manual hyperparameter adjustment
Solution Approach 1:
The scaling invariance linear layer automatically maintains the modulus length of weight parameters constant during training through its inherent mathematical properties. This self-service mechanism eliminates the need for manual hyperparameter adjustment, making the system easier to operate and reducing the time required for adjustments.
Solution Approach 2:
Instead of adjusting hyperparameters to control modulus length changes, the patent inverts the approach by designing a layer that inherently maintains fixed modulus length. This inversion transforms the problem from active control to passive maintenance, significantly easing operation.
3Measurement precision
If the search space for hyperparameters is large, then comprehensive model tuning is possible, but hyperparameter search costs increase significantly
Solution Approach 1:
The patent extracts the modulus length control function from the hyperparameter search space by embedding it in the scaling invariance linear layer's mathematical structure. This extraction reduces the search space to essentially one dimension (the target modulus length value), dramatically reducing search costs while maintaining model precision.
Solution Approach 2:
By changing from traditional hyperparameter optimization to modulus length constraint optimization, the patent transforms a high-dimensional search problem into a low-dimensional one. The scaling invariance property ensures that model precision is maintained while search costs are significantly reduced.
Data Source
AI summary
This application relates to the field of artificial intelligence technologies, and describes a classification model training method, a hyperparameter search method, and an apparatus. The training method includes obtaining a target hyperparameter of a to-be-trained classification model. The target hyperparameter is used to control a gradient update operation of the to-be-trained classification model. The to-be-trained classification model includes a scaling invariance linear layer. The scaling invariance linear layer enables a predicted classification result output when a weight parameter of the to-be-trained classification model is multiplied by any scaling coefficient to remain unchanged. The method further includes updating the weight parameter of the to-be-trained classification model based on the target hyperparameter and a target training manner, to obtain a trained classification model.