A method to improve the computational performance of deep learning models

With the wide application of deep learning in various fields, the computing performance and computing cost of models are required to be higher. This paper introduces some methods to improve the computational performance and reduce the computational cost of deep learning models, including pruning and mixed precision training. We verify the effectiveness of these methods through experiments, and discuss their advantages and disadvantages and application scenarios. The experimental results show that the pruning mixture can not only improve the precision, accuracy and efficiency of the training model, but also improve the performance of the model on multiple data sets, which provides a valuable reference for training and optimization of more complex deep learning models that require a lot of computing power.