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Open AccessArticle

Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer

Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Jixie Building 405 of West Campus, Huaxi District, Guiyang 550025, China
Author to whom correspondence should be addressed.
Algorithms 2018, 11(3), 28;
Received: 25 December 2017 / Revised: 5 March 2018 / Accepted: 5 March 2018 / Published: 7 March 2018
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
PDF [3376 KB, uploaded 9 March 2018]


This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%. View Full-Text
Keywords: convolutional neural network; activate function; dropout; SGD optimizer convolutional neural network; activate function; dropout; SGD optimizer

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Yang, J.; Yang, G. Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer. Algorithms 2018, 11, 28.

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