Deep neural network compression method based on reinforcement learning
A technology of deep neural network and reinforcement learning, which is applied in the direction of neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of time-consuming models, inability to directly run volume deep neural network models, and inflexibility, etc., to achieve improved compression Scale, wide application, reduction in storage space and computation requirements
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[0036] Example 1:
[0037] A deep neural network compression method based on reinforcement learning, including:
[0038] Step S100: Training a reinforcement learning agent based on the trained convolutional neural network, the reinforcement learning agent is used to identify network sparsity, and a compression method is used to compress the model;
[0039] Step S200: Train the compressed model again, optimize the parameters of the model, and obtain the final compressed model.
[0040] This method involves an input module, used to read the trained convolutional neural network model, including network structure, variable value, variable index and other information, as the data source of the next module;
[0041] The reinforcement learning module, based on the trained convolutional neural network model, trains a reinforcement learning agent that can recognize the sparsity of the network;
[0042] The compression module uses the reinforcement learning agent to judge the sparsity of each laye...
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