检索结果-内蒙古大学图书馆

2018年

作者： Rezaul Karim

ISBN: (数字)9781789130423

ISBN: (纸本)9781789136913

Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with tensorflow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book Description Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. tensorflow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with tensorflow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of tensorflow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn tensorflow features in a real-life problem, followed by detailed tensorflow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear

关键词： tensorflow smart predictions training models automated decisions machine learning algorithms predictive analytics MNIST deep MNIST predictive models machine learning

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Studying the Impact of tensorflow and PyTorch Bindings on Machine Learning Software Quality

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2025年第1期34卷 1-31页

作者： Li, Hao Rajbahadur, Gopi krishnan Bezemer, Cor-paul Univ Alberta Analyt Software GAmes & Repository Data ASGAARD La Edmonton AB Canada Huawei Canada Ctr Software Excellence Kingston ON Canada

Bindings for machine learning frameworks (such as tensorflow and PyTorch) allow developers to integrate a framework's functionality using a programming language different from the framework's default language (usually Python). In this article, we study the impact of using tensorflow and PyTorch bindings in C#, Rust, Python and JavaScript on the software quality in terms of correctness (training and test accuracy) and time cost (training and inference time) when training and performing inference on five widely used deep learning models. Our experiments show that a model can be trained in one binding and used for inference in another binding for the same framework without losing accuracy. Our study is the first to show that using a non-default binding can help improve machine learning software quality from the time cost perspective compared to the default Python binding while still achieving the same level of correctness.

关键词： Software engineering for machine learning software quality deep learning binding tensorflow PyTorch

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Cover Crop Biomass Predictions with Unmanned Aerial Vehicle Remote Sensing and tensorflow Machine Learning

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DRONES 2025年第2期9卷 131-131页

作者： Poudel, Aakriti Burns, Dennis Adhikari, Rejina Duron, Dulis Hendrix, James Gentimis, Thanos Tubana, Brenda Setiyono, Tri Louisiana State Univ Sch Plant Environm Soil Sci Baton Rouge LA 70803 USA Louisiana State Univ Agr Ctr NE Res Stn St Joseph LA 71366 USA Louisiana State Univ Dept Expt Stat Baton Rouge LA 70803 USA

The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras tensorflow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m-2 root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R2 also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m-2. The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth.

关键词： cover crops biomass vegetation indices canopy height tensorflow deep learning

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Tensor Train Decomposition on tensorflow (T3F)

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JOURNAL OF MACHINE LEARNING RESEARCH 2020年第1期21卷 1-7页

作者： Novikov, Alexander Izmailov, Pavel Khrulkov, Valentin Figurnov, Michael Oseledets, Ivan Natl Res Univ Higher Sch Econ Moscow Russia RAS Inst Numer Math Moscow Russia Cornell Univ Ithaca NY USA Skolkovo Inst Sci & Technol Moscow Russia

Tensor Train decomposition is used across many branches of machine learning. We present T3F a library for Tensor Train decomposition based on tensorflow. T3F supports GPU execution, batch processing, automatic differentiation, and versatile functionality for the Riemannian optimization framework, which takes into account the underlying manifold structure to construct efficient optimization methods. The library makes it easier to implement machine learning papers that rely on the Tensor Train decomposition. T3F includes documentation, examples and 94% test coverage.

关键词： tensor decomposition tensor train software gpu tensorflow

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tensorflow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews

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IEEE ACCESS 2019年 7卷 61018-61023页

作者： Suen, Hung-Yue Hung, Kuo-En Lin, Chien-Liang Natl Taiwan Normal Univ Dept Technol Applicat & Human Resource Dev Taipei 106 Taiwan Natl Chengchi Univ Dept Management Informat Syst Taipei 116 Taiwan

With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this paper, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a tensorflow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the "big five" traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to APR despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.

关键词： Big five convolutional neural network (CNN) personality computing tensorflow

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KHNN: Hypercomplex-valued neural networks computations via Keras using tensorflow and PyTorch

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SoftwareX 2025年 30卷

作者： Agnieszka Niemczynowicz Radosław A. Kycia Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Cracow PL31-155 Poland

Neural networks that utilize algebras more advanced than real numbers, such as hypercomplex numbers, can outperform traditional models in certain applications, usually, in the number of training parameters giving the same accuracy. However, no general framework exists for constructing hypercomplex neural networks. We propose a library integrated with Keras, tensorflow, and PyTorch, enabling computations within these advanced algebraic systems. The library offers Dense and Convolutional layer architectures for 1D, 2D, and 3D data, tailored to support hypercomplex operations. This tool provides a streamlined approach for developing models that leverage hypercomplex numbers, enhancing performance in areas like image processing and signal analysis, and fostering innovation in machine learning. The branch of this software – HypercomplexKeras – is the Keras extension for hypercomplex neural networks.

关键词： Hypercomplex Dense neural network Convolutional neural network Keras tensorflow PyTorch

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Optimal distributed parallel algorithms for deep learning framework tensorflow

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APPLIED INTELLIGENCE 2022年第4期52卷 3880-3900页

作者： Xie, Yuanlun He, Majun Ma, Tingsong Tian, Wenhong Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

Since its release, the tensorflow framework has been widely used in various fields due to its advantages in deep learning. However, it is still at its early state. Its native distributed implementation has difficulty in expanding for large models because it has issues of low utilization of multiple GPUs and slow distribution compared with running on single machine. It is of great significance to reduce the training time through parallel models. In view of this, we firstly provided an in-depth analysis of the implementation principle of tensorflow and identify the bottlenecks of its native distributed parallel models to improve. Then, two optimal algorithms are designed and implemented based on data parallelism and model parallelism modes of tensorflow. For data parallelism, the proposed algorithm is implemented to replace the native linear execution mode with pipeline execution mode. As for model parallelism, the native random partitioning mode is replaced by our proposed novel greedy algorithm. Finally, we built a homogeneous distributed cluster and a heterogeneous distributed cluster respectively to verify the effectiveness of the proposed algorithms. Through a number of comparative experiments, we showed that the proposed optimal parallel algorithms can effectively reduce model training time by an average of 26.5%(or average 1.5x speedup than native distributed algorithms) and improve the utilization of the cluster while keeping the same accuracy level of native tensorflow.

关键词： Deep learning tensorflow Data parallelism Model parallelism Optimal distributed parallel algorithms

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The research of virtual face based on Deep Convolutional Generative Adversarial Networks using tensorflow

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 521卷 667-680页

作者： Liu, Shouqiang Yu, Mengjing Li, Miao Xu, Qingzhen South China Normal Univ Guangzhou Sch Phys & Telecommun Engn Guangzhou Guangdong Peoples R China South China Normal Univ Guangzhou Sch Comp Sci Guangzhou Guangdong Peoples R China

Since Generative Adversarial Nets (GANs) has been proposed in 2014, it has become one of the most popular hot topics. Deep Convolutional Generative Adversarial Networks (DCGAN) is greatly promoted the development and application of GANs. In this paper, we have made an in-depth exploration for the most popular DCGAN at present via utilizing tensorflow deep learning framework, using the open CelebA face dataset of The Chinese University of Hong Kong as the data source. By comparing DCGAN unconstrained and DCGAN constrained, the experimental results show that the DCGAN model significantly improves the virtual face generation model after adding constraints in the training phase, which enhance the ability of the generator to deceive the discriminator. Finally, we have evaluated the proposed model from the perspective of TensorBoard and achieved the desired experimental results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Deep Convolutional Generative Adversarial Networks (DCGAN) tensorflow CelebA Virtual face

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Accelerating geostatistical seismic inversion using tensorflow: A heterogeneous distributed deep learning framework

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COMPUTERS & GEOSCIENCES 2019年第Mar.期124卷 37-45页

作者： Liu, Mingliang Grana, Dario Univ Wyoming Dept Geol & Geophys 1000 E Univ Ave Laramie WY 82071 USA

Geostatistical seismic inversion is one of emerging technologies in reservoir characterization and reservoir uncertainty quantification. However, the challenge of intensive computation often restricts its application in practical studies. To circumvent the computational limitation, in this work, we present a distributed parallel approach using tensorflow to accelerate the geostatistical seismic inversion. The approach provides a general parallel scheme to efficiently take advantage of all the available computing resources, i.e. CPUs and GPUs: the computational workflow is expressed and organized as a Data Flow Graph, and the graph can be divided into several sub-graphs which are then mapped to multiple computing devices to concurrently evaluate the operations in them. The high-level interface and the feature of automatic differentiation provided by tensorflow also makes it much easy for users to implement their algorithms in an efficient parallel manner, and allows employing programs on any computing platform almost without alteration. The proposed method was validated on a 3D seismic dataset consisting of 600 x 600 x 200 grid nodes. The results indicate that it is feasible to the practical application and the computational time can be largely reduced by using multiple GPUs.

关键词： Seismic inversion Geostatistics tensorflow GPU

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TfELM: Extreme Learning Machines framework with Python and tensorflow

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SOFTWAREX 2024年 27卷

作者： Struniawski, Karol Kozera, Ryszard Warsaw Univ Life Sci SGGW Inst Informat Technol ul Nowoursynowska 166 PL-02787 Warsaw Poland Univ Western Australia Sch Phys Math & Comp 35 Stirling Highway Perth WA 6009 Australia

TfELM introduces an innovative Python framework leveraging tensorflow for Extreme Learning Machines (ELMs), offering a comprehensive suite for diverse machine learning (ML) tasks. Existing solutions in the ELM landscape lack comprehensive implementations. TfELM fills this gap by consolidating 18 ELM variants (including 14 so-far unimplemented in Python) into a unified framework. It conforms to established scikit-learn standards and emphasizes modularity, facilitating seamless integration into ML pipelines. Harnessing tensorflow's GPU acceleration, TfELM ensures rapid training and compatibility across varied computing environments. Notably, TfELM marks the inaugural ELM implementation in tensorflow 2 , featuring high-performance model saving/loading via HDF5 format, thus enhancing its novelty and alignment with contemporary standards. Performance evaluations demonstrate that TfELM outperforms other solutions, achieving significant speed enhancements across various computing platforms, with improvements of up to nine times tested on five standard UCI datasets.

关键词： Extreme Learning Machine Machine learning Artificial intelligence Neural networks Python tensorflow

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