Parallel deep convolutional neural network optimization method based on Spark and AMPSO

The invention proposes a parallel deep convolutional neural network optimization method based on Spark and AMPSO, and the method comprises the following steps: S1, carrying out the pre-training of a DCNN model on a main node through employing an original data set, carrying out the pruning of the pre-trained DCNN model through employing an FP-FIS strategy, obtaining a compressed DCNN model, and distributing the compressed DCNN model to each calculation node; s2, dividing the original data set into data sets with the same size, and distributing the data sets to each computing node; s3, calling a map function and an MPT-AMPSO strategy on each mapper node, and performing parallel training on the compressed DCNN model by using the data set distributed on each node; and S4, calling a reduce ByKey function and a DLBNP strategy on each reduce node to carry out parallel updating on the compressed DCNN model parameters, and finally obtaining a parameter updating result. According to the invention ...