The training efficiency and test accuracy are important factors in judging the scalability of distributed deep learning.In this dissertation,the impact of noise introduced in the mixed national institute of standards ...The training efficiency and test accuracy are important factors in judging the scalability of distributed deep learning.In this dissertation,the impact of noise introduced in the mixed national institute of standards and technology database(MNIST)and CIFAR-10 datasets is explored,which are selected as benchmark in distributed deep learning.The noise in the training set is manually divided into cross-noise and random noise,and each type of noise has a different ratio in the dataset.Under the premise of minimizing the influence of parameter interactions in distributed deep learning,we choose a compressed model(SqueezeNet)based on the proposed flexible communication method.It is used to reduce the communication frequency and we evaluate the influence of noise on distributed deep training in the synchronous and asynchronous stochastic gradient descent algorithms.Focusing on the experimental platform TensorFlowOnSpark,we obtain the training accuracy rate at different noise ratios and the training time for different numbers of nodes.The existence of cross-noise in the training set not only decreases the test accuracy and increases the time for distributed training.The noise has positive effect on destroying the scalability of distributed deep learning.展开更多
This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an a...This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.展开更多
Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Com...Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.展开更多
文摘The training efficiency and test accuracy are important factors in judging the scalability of distributed deep learning.In this dissertation,the impact of noise introduced in the mixed national institute of standards and technology database(MNIST)and CIFAR-10 datasets is explored,which are selected as benchmark in distributed deep learning.The noise in the training set is manually divided into cross-noise and random noise,and each type of noise has a different ratio in the dataset.Under the premise of minimizing the influence of parameter interactions in distributed deep learning,we choose a compressed model(SqueezeNet)based on the proposed flexible communication method.It is used to reduce the communication frequency and we evaluate the influence of noise on distributed deep training in the synchronous and asynchronous stochastic gradient descent algorithms.Focusing on the experimental platform TensorFlowOnSpark,we obtain the training accuracy rate at different noise ratios and the training time for different numbers of nodes.The existence of cross-noise in the training set not only decreases the test accuracy and increases the time for distributed training.The noise has positive effect on destroying the scalability of distributed deep learning.
基金supported by the National Key R&D Program of China under Grant 2018AAA0101502.
文摘This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.
文摘Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.
