CNN (Convolutional Neural Network) code testing method and apparatus

Abstract

The invention provides a CNN (Convolutional Neural Network) code testing method and apparatus. The testing method comprises: controlling a convolutional layer, a down-sampling layer and a full-connection layer, in a CNN, realized by CNN codes to serve as to-be-tested layers separately; and testing a code segment through a testing logic of changing an element value in a matrix as input of the code segment for realizing functions of the to-be-tested layers in the CNN codes and then judging whether the element value of the corresponding position in a matrix as output of the code segment is changed or not, thereby testing functions of the CNN codes. According to the testing method and apparatus, the CNN codes can be tested and the blank in the aspect of CNN code testing in the prior art is filled up.

Description

【Technical field】 [0001] The invention relates to the technical field of software, in particular to a CNN code testing method and device. 【Background technique】 [0002] Deep learning is a popular technology in the field of big data analysis, and is widely used in artificial intelligence fields such as image recognition, speech recognition, and natural language understanding. The core of the deep learning algorithm is its prediction module, and a classic model to realize the prediction module is a convolutional neural network (CNN). [0003] From the perspective of code, in the code implementation of deep learning-based applications, the code that implements CNN functions (referred to as CNN code) is the core part of the entire code, so the CNN code is tested to ensure its correctness, and the entire code Quality plays a key role. However, since deep learning algorithms output probabilistic values, traditional black-box testing methods, etc. are not suitable for CNN codes....

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Problems solved by technology

However, since the output of the deep learning algorithm is a probability val

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