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Convolutional neural network model compression method and device, storage medium and electronic device

A convolutional neural network and neural network model technology, applied in the computer field, can solve problems such as poor flexibility and low efficiency of neural network models

Active Publication Date: 2019-07-19
TENCENT TECH (SHENZHEN) CO LTD
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Problems solved by technology

[0005] Embodiments of the present invention provide a convolutional neural network model compression method and device, storage medium, and electronic device to at least solve the technical problems of low efficiency and poor flexibility of neural network models in the related art

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Embodiment Construction

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0040] It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate ...

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Abstract

The invention discloses a convolutional neural network model compression method and device, a storage medium and an electronic device. The method comprises the following steps of merging the parameters of a first batch of standardized layers in a convolutional neural network model into a first convolutional layer in the convolutional neural network model to generate a first target neural network model comprising a first target convolutional layer, the convolutional neural network model and the first target neural network model having the same output for the same input; deleting a convolution kernel with a norm smaller than a first threshold value in a first target convolution layer in the first target neural network model to obtain a second target neural network model; and compressing thesecond target neural network model to obtain a third target neural network model, so that the technical problems of low use efficiency and poor flexibility of a neural network model in the prior art are solved.

Description

technical field [0001] The invention relates to the field of computers, in particular to a method and device for compressing a convolutional neural network model, a storage medium and an electronic device. Background technique [0002] In related technologies, the neural network model has a huge amount of parameters, and it is difficult to directly apply it to end products. [0003] In addition, due to the huge amount of parameters of the neural network model and high memory consumption, the use efficiency of the neural network model is low. Further, due to the high memory consumption, it is only allowed to be used in a fixed scene, resulting in poor flexibility of using the neural network model. [0004] For the above problems, no effective solution has been proposed yet. Contents of the invention [0005] Embodiments of the present invention provide a method and device for compressing a convolutional neural network model, a storage medium, and an electronic device, so ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04
CPCG06N3/045Y02D10/00
Inventor 金坤李峰赵世杰左小祥
Owner TENCENT TECH (SHENZHEN) CO LTD
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