查看文章

Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and has shown excellent results in a variety of applications such as medical field, consumer as well as autonomous vehicles. Convolutional Neural Network (CNN)–is the leading deep learning architecture that is mostly applied. However, huge dataset is needed to train with complex architecture to achieve precise learning. Inference can be performed when given a ready CNN model and its weight file to another user. Inference takes time with precise weights and huge dataset. To overcome this problem, and enhance the inference system, approximation computation will be applying in terms of weight for changed of decimal place. The smaller size of the dataset is used in the inference process to reduce the inference time. MobileNetV2 architecture is used with the new weight for inference. Also, open source libraries such as …