Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches
This paper proposes a method to find the hyperparameter tuning for a deep neural network by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters for such a neural network is difficult because the neural network that has several parameters to configure; furthermore,...
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| Veröffentlicht in: | Knowledge-based systems Jg. 178; S. 74 - 83 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
15.08.2019
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | This paper proposes a method to find the hyperparameter tuning for a deep neural network by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters for such a neural network is difficult because the neural network that has several parameters to configure; furthermore, the training speed for such a network is slow. The proposed method was tested for two neural network models; an autoencoder and a convolution neural network with the Modified National Institute of Standards and Technology (MNIST) dataset. To optimize hyperparameters with the proposed method, the cost functions were selected as the average of the difference between the decoded value and the original image for the autoencoder, and the inverse of the evaluation accuracy for the convolution neural network. The hyperparameters were optimized using the proposed method with fast convergence speed and few computational resources, and the results were compared with those of the other considered optimization algorithms (namely, simulated annealing, genetic algorithm, and particle swarm algorithm) to show the effectiveness of the proposed methodology.
•An optimization method for hyper-parameters for a deep neural network.•Performing optimization of the network using a univariate dynamic encoding algorithm for searches.•Validation of the proposed method with two neural network model with MNIST data set.•Fast convergence speed and a small computational amount to optimize hyper-parameter of the network. |
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| AbstractList | This paper proposes a method to find the hyperparameter tuning for a deep neural network by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters for such a neural network is difficult because the neural network that has several parameters to configure; furthermore, the training speed for such a network is slow. The proposed method was tested for two neural network models; an autoencoder and a convolution neural network with the Modified National Institute of Standards and Technology (MNIST) dataset. To optimize hyperparameters with the proposed method, the cost functions were selected as the average of the difference between the decoded value and the original image for the autoencoder, and the inverse of the evaluation accuracy for the convolution neural network. The hyperparameters were optimized using the proposed method with fast convergence speed and few computational resources, and the results were compared with those of the other considered optimization algorithms (namely, simulated annealing, genetic algorithm, and particle swarm algorithm) to show the effectiveness of the proposed methodology.
•An optimization method for hyper-parameters for a deep neural network.•Performing optimization of the network using a univariate dynamic encoding algorithm for searches.•Validation of the proposed method with two neural network model with MNIST data set.•Fast convergence speed and a small computational amount to optimize hyper-parameter of the network. This paper proposes a method to find the hyperparameter tuning for a deep neural network by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters for such a neural network is difficult because the neural network that has several parameters to configure; furthermore, the training speed for such a network is slow. The proposed method was tested for two neural network models; an autoencoder and a convolution neural network with the Modified National Institute of Standards and Technology (MNIST) dataset. To optimize hyperparameters with the proposed method, the cost functions were selected as the average of the difference between the decoded value and the original image for the autoencoder, and the inverse of the evaluation accuracy for the convolution neural network. The hyperparameters were optimized using the proposed method with fast convergence speed and few computational resources, and the results were compared with those of the other considered optimization algorithms (namely, simulated annealing, genetic algorithm, and particle swarm algorithm) to show the effectiveness of the proposed methodology. |
| Author | Yoo, YoungJun |
| Author_xml | – sequence: 1 givenname: YoungJun surname: Yoo fullname: Yoo, YoungJun email: youdalj@postech.ac.kr organization: Department of Electronic Engineering, Pohang University of Science and Technology (POSTECH), San 31, Hyojadong, Namgu, Pohang, Gyungbuk, 790-784, Republic of Korea |
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| Keywords | Hyperparameter optimization Deep neural network Convolution neural network Autoencoder Gradient-free optimization |
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| SubjectTerms | Algorithms Artificial neural networks Autoencoder Averages Computer simulation Convergence Convolution Convolution neural network Deep neural network Encoding Function words Genetic algorithms Genetics Gradient-free optimization Hyperparameter optimization Networks Neural networks Optimization Searching Simulated annealing |
| Title | Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches |
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