Model generation method and device

A technology of model generation and model structure, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as unsatisfactory distillation effect, unsuitable distillation, mismatch between small network and large network, etc.

Pending Publication Date: 2020-02-07
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Problems solved by technology

However, the model structure of the artificially designed small network may not be suitable for distillation, or the structure of the

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  • Model generation method and device
  • Model generation method and device

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Embodiment Construction

[0026] The present disclosure will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, rather than to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0027] It should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

[0028] figure 1 An exemplary system architecture 100 to which the model generation method or model generation apparatus of the present disclosure can be applied is shown.

[0029] figure 1 An exemplary system architecture 100 to which the model generation method or mo...

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Abstract

The invention relates to the technical field of artificial intelligence. The embodiment of the invention provides a model generation method and device. The method comprises the steps of obtaining a first neural network used for executing a deep learning task; searching a second neural network by executing multiple iterative operations; wherein the iterative operation comprises the steps of updating a preset model structure controller based on a current feedback reward value, and generating a candidate neural network by adopting the updated model structure controller; distilling the candidate neural network based on the first neural network, and determining a distillation loss function of the distilled candidate neural network; updating a reward feedback value based on a distillation loss function of the distilled candidate neural network; and determining the distilled candidate neural network obtained in the current iterative operation as the searched second neural network in responseto the determination that the reward feedback value reaches a preset convergence condition or the cumulative number of the iterative operations reaches a preset number threshold. According to the method, the neural network model structure suitable for distillation can be automatically searched.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of computer technology, specifically to the field of artificial intelligence technology, and especially to a method and device for generating a model. Background technique [0002] With the development of artificial intelligence technology, deep learning has achieved good results in many application fields. In deep learning, the structure of the neural network has a very important impact on the effect of the model. In practice, in order to obtain higher performance, the structural complexity of the neural network is relatively high, and more computing resources are required to run the neural network. However, manually designing the structure of the network requires a lot of experience and multiple attempts, and the cost is relatively high. [0003] Model distillation is a means of using a large model to supervise the training of a small network so that the small network can achieve the per...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/086G06N3/084G06N3/048G06N3/045
Inventor 希滕张刚温圣召
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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