Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition

Abstract

Neural network pruning methods can decrease the parameter counts of trained neural networks along with improving the computational performance of inference without compromising the accuracy. This work has demonstrated the effectiveness of the neural network pruning methodology, which was applied to deep learning-based human activity recognition. A long short-term memory (LSTM) architecture was used to recognize daily human activities from smartphone-based accelerometer data. An android-based application has been developed to collect the accelerometer data from smartphones. Data has been preprocessed before training the LSTM network. The accuracy of the proposed LSTM model was 97.2% while recognizing the human activities. Neural network pruning was applied to the model once the model was trained. After 70% of weight pruning on the trained/initial network, the Pruned network accuracy was similar (97.2%) as the original one. However, the accuracy of the pruned network was better than the initial model, while the network has been pruned by 25% (accuracy 97.26%), 50% (accuracy 97.29%), and 60% (accuracy 97.22%). The initial model started to slightly decline the accuracy (from 97.2% to 96.72%), while the model was pruned by 80%. In terms of neural/unit pruning, slightly declination in the accuracy (97.2% to 97.09%) occurred after 50% of pruning.