Neural Network Pruning for Hyperparameter Optimization
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Solution Overview
Problem
Deep learning model hyperparameter optimization (HPO) is time-consuming due to high computational costs and the difficulty in finding an optimal set of hyperparameters, especially with limited resources, and conventional pruning methods lead to inefficiencies and the layer collapse phenomenon.
Innovation Solution
A neural network learning apparatus and method that performs structured single-shot pruning on a base neural network, maintaining a minimum channel remaining ratio for each layer, and uses hyperparameter optimization techniques like random search, evolutionary optimization, or Bayesian optimization to efficiently obtain an optimized hyperparameter set for training the neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional pruning methods are used to reduce computational cost, then the amount of computation is reduced, but layer collapse phenomenon occurs and HPO time is extended
Solution Approach 1:
The patent performs pruning on the base neural network before HPO to obtain a pruned neural network with reduced computational cost. This preliminary action enables faster HPO iterations while maintaining model performance, directly resolving the contradiction between reducing computational cost and avoiding time extension in HPO.
Solution Approach 2:
The patent changes the pruning ratio parameter to control the balance between computational cost reduction and performance maintenance. By adjusting the pruning ratio, the system achieves efficient HPO without causing layer collapse, resolving the time loss issue while maintaining energy efficiency.
2Loss of energy
If the entire layer of the neural network is pruned during pruning, then computational cost is reduced, but layer collapse phenomenon occurs
Solution Approach 1:
The patent applies local quality by pruning only specific channels within layers rather than entire layers. This selective pruning maintains the structural integrity of each layer while reducing overall computational cost, preventing layer collapse and ensuring model reliability.
Solution Approach 2:
The patent uses partial action by pruning only a portion of the neural network (specific channels) rather than the entire network. This partial pruning approach reduces computational cost without causing layer collapse, as essential channels are preserved in each layer.
3Measurement precision
If HPO is performed repeatedly to find optimal hyperparameters, then optimization accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary pruning on the base neural network before initiating HPO. This preliminary action creates a more efficient pruned neural network that requires fewer HPO iterations to converge, achieving high optimization accuracy with reduced time consumption.
Solution Approach 2:
The patent uses the pruned neural network as a foundation for HPO, effectively copying the optimized structure into the HPO process. This allows HPO to focus only on hyperparameter tuning rather than retraining the entire network, significantly reducing time while maintaining accuracy.
Data Source
AI summary
A neural network learning apparatus includes a pruning unit configured to obtain a pruned neural network by pruning on a base neural network. The neural network learning apparatus also includes an optimizing unit configured to obtain an optimized hyperparameter set by performing hyperparameter optimization (HPO) a predetermined number of times using the pruned neural network. The neural network learning apparatus additionally includes a learning unit configured to train the base neural network using the optimized hyperparameter set.