Training method and apparatus of network models, equipment, and storage medium

Abstract

The embodiment of the invention discloses a training method and apparatus of network models, equipment, and a storage medium. The training method of network models includes the steps: when a first network model achieves the preset update stopping condition, according to the update result of the first network model, determining the first target network model, and inserting a relay loss network layer in the preset pooling layer in the first target network model so as to determine the second network model; according to the second network model and the relay loss network layer, determining the relay loss function corresponding to the relay loss network layer; according to the second network model and the relay loss function, determining the global loss function of the second network model; andapplying the relay loss function and the global loss function to update the parameters of the second network model so as to obtain the updated second network model. The training method and apparatusof network models can solve the problem that during the network model training process, the high-layer characteristics are over fitting while the low layer characteristics are poor fitting, so that the network model training can become more thoroughly and has higher accuracy.

Description

technical field [0001] The present invention relates to the field of deep learning, in particular to a network model training method, device, equipment and storage medium. Background technique [0002] Existing face recognition models are usually trained based on deep learning algorithm models, and the quality of deep learning algorithm model training affects the results of face recognition. [0003] The current deep learning model for face recognition basically adds one or more loss function layers after the highest layer (feature layer) of the network to train and update the parameters of the deep learning network model. The existing technology usually adds one or more loss functions after the feature layer of the network for training, but during the training process, due to the long parameter transmission path, the high-level features are over-fitted and the middle-level features are under-fitted, resulting in The training of the entire network is not thorough enough, wh...

AI Technical Summary

Problems solved by technology

The existing technology usually adds one or more loss functions after the feature layer of the network for training, but during the training process, due to the long parameter transmission path, the high-level features are over-fitted and the middle-level features are under-fitted, resulting in The training of the entire network is not thorough enough, which leads to the inability to update the parameters of the middle layer of the deep network learning model network, which leads to unsatisfactory processing results of the trained model in actual face recognition

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