Comprehensive analysis of the ability to monitor selected optical network parameters in the physical layer using convolutional neural networks

2018 ◽  
Author(s):  
Tomasz Mrozek ◽  
Krzysztof Perlicki
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Optical Network ◽  
Physical Layer ◽  
Comprehensive Analysis ◽  
Network Parameters

scholarly journals OPTIMAL UNIFORM QUANTIZATION OF PARAMETERS OF CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
pp. 99-103
Author(s):  
D. S. Kolesnikov ◽  
D. A. Kuznetsov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mobile Applications ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Network Parameters ◽  
Wide Range ◽  
Uniform Quantization ◽  
Adaptive Step

State of the art convolutional neural networks provide high accuracy in solving a wide range of problems. Usually it is achieved by a significant increasing their computational complexity and the representation of the network parameters in single-precision floating point numbers. However, due to the limited resources, the application of networks in embedded systems and mobile applications in real time is problematic. One of the methods to solve this problem is to reduce the bit depth of data and use integer arithmetic. For this purpose, the network parameters are quantized. Performing quantization, it is necessary to ensure a minimum loss of recognition accuracy. The article proposes to use an optimal uniform quantizer with an adaptive step. The quantizer step depends on the distribution function of the quantized parameters. It reduces the effect of the quantization error on the recognition accuracy. There are also described approaches to improving the quality of quantization. The proposed quantization method is estimated on the CIFAR-10 database. It is shown that the optimal uniform quantizer for CIFAR-10 database with 8-bit representation of network parameters allows to achieve the accuracy of the initial trained network.

scholarly journals Selection of features system and network parameters for hyperspectral images classification using convolutional neural networks

2021 ◽  
Author(s):  
V.I. Kozik ◽  
E.S. Nezhevenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Correct Classification ◽  
Network Parameters ◽  
Input Layer ◽  
Selection Of ◽  
Classified Image

A classification system for hyperspectral images using convolutional neural networks is described. A specific network was selected and analyzed. The network parameters, ensured the maximum classification accuracy: dimension of the input layer, number of the layers, size of the fragments into which the classified image is divided, number of learning epochs, are experimentally determined. High percentages of correct classification were obtained with a large-format hyperspectral image, and some of the classes into which the image is divided are very close to each other and, accordingly, are difficult to distinguish by hyperspectra.

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