Deep learning model-oriented dynamic test method and device

Abstract

The invention discloses a deep learning model-oriented dynamic test method. The method comprises the following steps: S1, obtaining a picture data set and a deep learning model; S2, dividing the picture data set into a training set and a test set, and training the deep learning model by using the training sample to obtain a classification model; S3, randomly selecting pictures from the test set astest seed samples; S4, inputting the test seed samples into a classification model, if a classification result output by the classification model is inconsistent with a label of the test seed sample,taking the test seed samples as test samples, and if not, entering step S5; S5, calculating a gradient based on the cross entropy loss and the neuron coverage rate of the test seed samples in the classification model; S6, modifying the test seed samples according to a gradient rising algorithm; S7, circularly executing steps S4 to S6 until all the test seed samples become test samples and are output; and S8, evaluating the classification performance of the model by using the test samples.

Description

technical field [0001] The invention relates to the technical field of testing, in particular to a deep learning model-oriented dynamic testing method and a device thereof. Background technique [0002] Deep learning has gradually become a research hotspot and mainstream development direction in the field of artificial intelligence. Deep learning is a computational model composed of multiple processing layers, a machine learning technique that learns data representations with multiple levels of abstraction. Deep learning represents the main development direction of machine learning and artificial intelligence research, and has brought revolutionary progress to the fields of machine learning and computer vision. [0003] Artificial intelligence technology has made breakthroughs in fields such as computer vision and natural language processing, ushering in a new round of explosive development of artificial intelligence. Deep learning is key to these breakthroughs. Among the...

AI Technical Summary

Problems solved by technology

[0008] Most existing testing techniques for autonomous driving rely on manual collection of test data for different driving conditions, which becomes unacceptably expensive as test scenarios increase

At the same time, the existing testing techniques are based on counting the number of activated neurons to generate test samples, which is a static testing process, and there are problems such as long time consumption and low efficiency in generating test sets.

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