China
Africa
Explore chapters and articles related to this topic
Deep Learning Architecture and Framework
Published in Krishna Kant Singh, Vibhav Kumar Sachan, Akansha Singh, Sanjeevikumar Padmanaban, Deep Learning in Visual Computing and Signal Processing, 2023
Ashish Tripathi, Shraddha Upadhaya, Arun Kumar Singh, Krishna Kant Singh, Arush Jain, Pushpa Choudhary, Prem Chand Vashist
TensorFlow is used for image processing, speech recognition, and text analysis. It is used in programming languages, such as Python, Go, C++, C, Java and so on. PyTorch framework is used in Python programming language. It is used for image classification, text generation, and for many more purposes. MXNet framework was developed by Apache. It is used in many programming languages such as Julia, Go, JavaScript, MATLAB, R, Scala, and Perl. It is supposed to support flexible programming model in many languages. Other frameworks such as Keras, DeepLearning4j, Deeplearn.js, and Microsoft Cognitive Toolkit are used in various networks. All these networks are used for various applications according to their functionality. Hence, to build an efficient and accurate network, an appropriate network and framework are being chosen. Each network and framework have their own advantages and by choosing correct network and framework, an efficient and accurate model can be developed.
Inclusion of Impaired People in Industry 4.0
Published in Roshani Raut, Salah-ddine Krit, Prasenjit Chatterjee, Machine Vision for Industry 4.0, 2022
Martín Montes Rivera, Alberto Ochoa Zezzatti, Luis Eduardo de Lira Hernández
TensorFlow is a machine learning method that works in heterogeneous environments and on a wide scale. TensorFlow uses dataflow graphs to represent the shared state and its operations. It maps data flow graph nodes over several computers in a cluster and across many computing devices within a machine, including multicore CPUs, general-purpose GPUs and custom ASICs known as tensor processing units (TPUs). TensorFlow allows developers to experiment with new optimisations and training algorithms. TensorFlow serves several applications, emphasising deep neural networks training and inference. In development, many Google platforms use TensorFlow [27]. The diagram in Figure 6.5 shows the general schema followed in TensorFlow for training an input dataset.
Software
Published in Rémi Cresson, Deep Learning for Remote Sensing Images with Open Source Software, 2020
TensorFlow uses symbolic programming that distinguishes definitions of computations from their proper execution. In TensorFlow, tensors are abstraction objects of the operations and values in the memory, simplifying manipulation regardless of the computing environment: for instance, Central Processing Unit (CPU) or Graphical Processing Unit (GPU). In TensorFlow, the so-called model consists of operations arranged into a graph of nodes. Each node is an operation taking zero or more tensors as inputs, and producing one or multiple tensors. This data flow graph defines the operations (e.g. linear algebra operators), and the actual computations are performed in the TensorFlow session.
Displacement prediction of water-induced landslides using a recurrent deep learning model
Published in European Journal of Environmental and Civil Engineering, 2023
Qingxiang Meng, Huanling Wang, Mingjie He, Jinjian Gu, Jian Qi, Lanlan Yang
Before implementation of landslide displacement prediction, we give a brief introduction to the deep-learning codes used in the deep-learning model. Two open-source deep-learning frameworks, namely, TensorFlow and Keras, are applied in this work. TensorFlow was originally developed by Google for the purposes of conducting machine learning and deep neural network research (Abadi et al., 2016). This system is one of the most widely used deep-learning frameworks and is sufficiently general to be applicable in a wide variety of other domains as well. Keras is a high-level neural network API written in Python and is capable of running on top of a main deep-learning framework such as TensorFlow, CNTK, or Theano (François, 2015). In this work, we apply the deep-learning network using Keras running on TensorFlow. In addition to TensorFlow and Keras, selected other statistical software packages such as Pandas, Sklearn and Statsmodels are also used in the data analysis. The general process for a time series can be divided into 3 components, namely, data preprocessing, training and testing (Figure 3).
Bayesian estimation of discrete choice models: a comparative analysis using effective sample size
Published in Transportation Letters, 2022
Jason Hawkins, Khandker Nurul Habib
In addition to the methods explored in this paper, many advances are being made that are likely of interest to the transportation researcher. Other methods of approximate inference have been developed, such as operator variational inference (Ranganath et al. 2016) and expectation propagation (Vehtari et al. 2020). Expectation propagation is useful when dealing with large datasets because it partitions the problem into a set of local inferences rather than focusing on the global posterior. These algorithms, like ADVI, are useful tools for applying Bayesian methods to large datasets but remain relatively experimental – i.e., lacking in documentation and diagnostic tests. Other computational methods developed for machine learning are also seeing applications in Bayesian inference. TensorFlow is based on the representation of a model as a series of tensors forming a computational graph. This structuring of the model allows TensorFlow to run models on GPU and TPU, as well as employ emerging methods such as accelerated linear algebra (XLA) (TensorFlow developers 2020). These tools provide opportunities for transportation researchers to bridge the gap between classical statistics and machine learning through the development of Bayesian neural networks and other methods. However, they do require more advanced computing resources and coding expertise beyond that required for implementation of similar models in Stan.
Client profile prediction using convolutional neural networks for efficient recommendation systems in the context of smart factories
Published in Enterprise Information Systems, 2022
Nadia Nedjah, Victor Ribeiro Azevedo, Luiza De Macedo Mourelle
For the development of the training model, a virtual environment is created with the container docker. A container docker is a standard encapsulated environment that runs applications through virtualisation at the operating system level. An image of docker is a file used to execute code in a docker container. An image is an immutable file that is essentially the representation of a container. Images are created with the compilation command and they will produce a container when started with execution. The (Maksimov 2019) image of a docker container is used with the following tools: Jupyter, Matplotlib, Pandas, Tensorflow, Keras and OpenCV. In this work, the Tensorflow and the Keras are used with the language Python to perform the training. Note that Tensorflow is an open source software library for machine intelligence created by Google in 2015. With Tensorflow, it is possible to define machine learning models, train them with data, and export them. Tensorflow operates with tensors that are multidimensional vectors running through the nodes of a graph.