Improved Adaptive Genetic-Algorithm based Structured Pruning Method
Network pruning is one of the main methods for compressing deep neural network models.To address the issue of low efficiency in existing genetic algorithm-based network pruning methods,we propose a structured pruning method based on an improved adaptive genetic algorithm.Firstly,we design a new fitness function that balances the impact of model loss and parameter quantity of the final result.Secondly,adaptive crossover and mutation probabilities are used instead of fixed hyperparameters to improve pruning efficiency and model accuracy.Finally,the feasibility of the method is verified through experiments,obtaining network models with higher accuracy and fewer parameters.
deep learningmodel compressionnetwork pruninggenetic algorithm
NETL
NSTL
万方数据
