scholarly journals Specificities of data set preparation and neural network training for object recognition

2021 ◽  
pp. 146-151
Author(s):  
Dmytro Kyrychuk ◽  
Andriy Segin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Recognition ◽  
Neural Network Training ◽  
Data Set ◽  
Network Training ◽  
Object Based ◽  
Recognition Ability ◽  
Trained Network

The paper presents the results of the research on the expediency of training a neural network on images of different clarity and brightness using unevenly distributed lighting on a working area with statically positioned system elements. The use of transfer learning for neural networks to improve the accuracy of object recognition was justified. The object recognition ability of a convolutional neural network while scaling the object relatively to the original was researched. The results of the research on the influence of lighting on the quality of object recognition by a trained network and the influence of background choice for a working area on the quality of object-based feature selection are presented. Based on the results obtained, recommendations for the preparation of individual datasets to improve the quality of training and further object recognition of convolutional neural networks through the elimination of unnecessary variables in images were provided.

scholarly journals Blender as a tool for generating synthetic data

2020 ◽  
Vol 16 ◽  
pp. 227-232
Author(s):  
Rafał Sieczka ◽  
Maciej Pańczyk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synthetic Data ◽  
Image Data ◽  
Real Data ◽  
Neural Network Training ◽  
Data Set ◽  
Network Training ◽  
Significant Acceleration ◽  
Graphics Software

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data isdifficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generatesynthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained ona set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the resultsshowed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of thedata acquisition process and consequently, the learning process of neural networks.

scholarly journals Machine-learning in astronomy

2014 ◽  
Vol 10 (S306) ◽  
pp. 279-287 ◽  
Author(s):  
Michael Hobson ◽  
Philip Graff ◽  
Farhan Feroz ◽  
Anthony Lasenby
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Gamma Ray ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Data Description ◽  
Astronomical Data ◽  
Machine Learning Methods ◽  
Network Training

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

scholarly journals Parallelization of Neural Network Training for NLP with Hogwild!

2017 ◽  
Vol 109 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Valentin Deyringer ◽  
Alexander Fraser ◽  
Helmut Schmid ◽  
Tsuyoshi Okita
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suitable Method ◽  
Neural Network Training ◽  
Training Time ◽  
Pos Tagging ◽  
Network Training ◽  
Speed Up

Abstract Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2022 ◽  
pp. 202-226
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Rate Minimum ◽  
Hidden Layer ◽  
Function Number

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

scholarly journals Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2020 ◽  
Vol 11 (4) ◽  
pp. 62-85
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Hidden Layer ◽  
Function Number ◽  
The Impact

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

Incremental Neural Network Training for Medical Diagnosis

2008 ◽  
pp. 720-731
Author(s):  
Sheng-Uei Guan ◽  
Ji Hua Ang ◽  
Kay Chen Tan ◽  
Abdullah Al Mamun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Incremental Learning ◽  
Medical Diagnosis ◽  
Incremental Algorithm ◽  
Neural Network Training ◽  
Network Training ◽  
Novel Method ◽  
Partitioning Algorithms

This chapter proposes a novel method of incremental interference-free neural network training (IIFNNT) for medical datasets, which takes into consideration the interference each attribute has on the others. A specially designed network is used to determine if two attributes interfere with each other, after which the attributes are partitioned using some partitioning algorithms. These algorithms make sure that attributes beneficial to each other are trained in the same batch, thus sharing the same subnetwork while interfering attributes are separated to reduce interference. There are several incremental neural networks available in literature (Guan & Li, 2001; Su, Guan & Yeo, 2001). The architecture of IIFNNT employed some incremental algorithm: the ILIA1 and ILIA2 (incremental learning with respect to new incoming attributes) (Guan & Li, 2001).

Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks

2012 ◽  
Vol 500 ◽  
pp. 198-203
Author(s):  
Chang Lin Xiao ◽  
Yan Chen ◽  
Lina Liu ◽  
Ling Tong ◽  
Ming Quan Jia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Soil Moisture ◽  
Training Data ◽  
Neural Network Training ◽  
Practical Test ◽  
Network Training

Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

Using Artificial Bee Colony to Improve Functional Link Neural Network Training

2012 ◽  
Vol 263-266 ◽  
pp. 2102-2108 ◽  
Author(s):  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Bee Colony ◽  
Neural Network Training ◽  
Functional Link ◽  
Bee Colony ◽  
Network Training ◽  
Artificial Neural ◽  
Functional Link Neural Network

Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

scholarly journals S-DFP: shifted dynamic fixed point for quantized deep neural network training

2021 ◽  
Author(s):  
Yasufumi Sakai ◽  
Yutaka Tamiya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fixed Point ◽  
Deep Neural Networks ◽  
Data Representation ◽  
Training Methods ◽  
Neural Network Training ◽  
Training Time ◽  
Network Training ◽  
Complex Models

AbstractRecent advances in deep neural networks have achieved higher accuracy with more complex models. Nevertheless, they require much longer training time. To reduce the training time, training methods using quantized weight, activation, and gradient have been proposed. Neural network calculation by integer format improves the energy efficiency of hardware for deep learning models. Therefore, training methods for deep neural networks with fixed point format have been proposed. However, the narrow data representation range of the fixed point format degrades neural network accuracy. In this work, we propose a new fixed point format named shifted dynamic fixed point (S-DFP) to prevent accuracy degradation in quantized neural networks training. S-DFP can change the data representation range of dynamic fixed point format by adding bias to the exponent. We evaluated the effectiveness of S-DFP for quantized neural network training on the ImageNet task using ResNet-34, ResNet-50, ResNet-101 and ResNet-152. For example, the accuracy of quantized ResNet-152 is improved from 76.6% with conventional 8-bit DFP to 77.6% with 8-bit S-DFP.

Sign in / Sign up

Export Citation Format

Share Document