Abstract: A method and device for model compression of a neural network. The method comprises: recording input and output parameters of each layer of network in a network structure; dividing the network structure into several small networks according to the input and output parameters; setting a pruning flag bit of a first convolutional layer in each small network to be zero to obtain a pruned small network; training each pruned small network to obtain a network weight and a weight mask; recording a pruned channel index number of each convolutional layer of a pruned small network with the weight mask of zero; and carrying out decomposition calculation on each pruned small network according to the pruned channel index number. According to the method, a calculation amount and the size of a model is reduced, and during network deployment, the model can be loaded with one click, thus reducing usage difficulty.

Abstract: A method and device for model compression of a neural network. The method comprises: recording input and output parameters of each layer of network in a network structure; dividing the network structure into several small networks according to the input and output parameters; setting a pruning flag bit of a first convolutional layer in each small network to be zero to obtain a pruned small network; training each pruned small network to obtain a network weight and a weight mask; recording a pruned channel index number of each convolutional layer of a pruned small network with the weight mask of zero; and carrying out decomposition calculation on each pruned small network according to the pruned channel index number. According to the method, a calculation amount and the size of a model is reduced, and during network deployment, the model can be loaded with one click, thus reducing usage difficulty.

Abstract: Disclosed are a method, apparatus and device for dynamically adjusting a neural network channel, and a computer-readable storage medium. The method comprises : selecting, on the basis of a preset pruning rule, initial pruned channels and initial unpruned channels of an initial deep neural network; after the initial deep neural network is trained, correcting a channel that is incorrectly pruned, and dynamically adjusting the number of network channels; cyclically executing the steps of training a network, correcting channel pruning, and determining whether the ratio of the number of current pruned channels to the total number of channels reaches a preset proportion threshold value, until the ratio of the number of current pruned channels to the total number of channels reaches the preset proportion threshold value; and obtaining a deep neural network after network channel pruning is completed, and training the deep neural network to obtain a target deep neural network.