Operator precision detection method and device based on deep learning framework
A technology of deep learning and accuracy detection, applied in the field of deep learning, can solve problems such as poor detection effect, high cost, and low efficiency of operator precision testing, and achieve the effect of improving detection efficiency and ensuring reliability
Active Publication Date: 2020-12-29
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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[0004] However, the deep learning framework has the characteristics of fast version iteration and a large number of operators. The unit test of operators usually involves multiple test samples. In related technologies, the method that requires operator accuracy testing is low in efficiency, high in cost and poor in detection effect
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[0031]The exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should realize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Likewise, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0032]In order to solve the technical problems of low efficiency, high cost and poor detection effect of operator accuracy detection methods based on deep learning frameworks in the prior art, this application proposes an operator accuracy detection method based on deep learning frameworks. Automatic detection in terms of value, reverse calculation accuracy, etc., to ...
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The invention discloses an operator precision detection method and device based on a deep learning framework, electronic equipment and a storage medium, and relates to the technical field of deep learning. According to the specific implementation scheme, the method comprises the steps of obtaining a preset test template corresponding to a unit test of an operator in a deep learning framework; analyzing the test template to obtain the maximum precision value of the unit test, and detecting whether the precision of the input data of at least one test sample in the unit test meets the maximum precision value or not; obtaining all reverse operators in the unit test according to a preset position identifier under the condition that the input data precision of at least one test sample meets themaximum precision value through detection; and detecting whether the reverse calculation precision of each reverse operator meets a preset condition or not according to the test template, and sendingan operator unit test detection success message under the condition that the reverse calculation precision of all the reverse operators meets the preset condition. Therefore, the operator precision detection efficiency is improved, and the reliability of the deep learning framework operator is ensured.
Description
Technical field[0001]This application relates to the field of deep learning technology in the field of data processing technology, and in particular to an operator accuracy detection method, device, electronic device, and storage medium based on a deep learning framework.Background technique[0002]Artificial intelligence is a discipline that studies the computer to simulate certain human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It has both hardware-level technology and software-level technology. Artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning , Big data processing technology, knowledge map tech...
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IPC IPC(8): G06N3/08G06N3/04
CPCG06N3/082G06N3/084G06N3/045
Inventor 高薇骆涛
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD