ner_tags: O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Model I-Model B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Model I-Model I-Model B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Model I-Model B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O B-Model I-Model B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model I-Model B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O B-Model B-Model I-Model I-Model B-Model B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O B-Model O O O O O O O O O O O O O O O O O B-Model B-Citation I-Citation I-Citation I-Citation O O B-Model B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O B-Model O O O O O O O O O O O O O B-Model O O O O O O O O O
tokens: such as the malfunction of satellites, radiation hazards for astronauts, and the interruption of radio communication and electric power (e.g., Schrijver et al. 2014 ; Ponomarchuk et al. 2015 ). Hence, it is very important to establish a reliable system for flare prediction to effectively prevent various types of damage caused by powerful flare events. The triggering mechanism of solar flares is not well understood, thus many flare prediction models have been developed with different methods in recent years. Some of them are based on statistical methods (Song et al. 2009 ; Mason Hoeksema 2010 ; Bloomfield et al. 2012 ; Barnes et al. 2016 ), while most of them are based on machine-learning methods. Traditional machine-learning methods commonly used for flare prediction cover artificial neural network (Qahwaji Colak 2007 ; Ahmed et al. 2013 ; Li Zhu 2013 ; Nishizuka et al. 2018 ), k-nearest neighbors (Li et al. 2008 ; Huang et al. 2013 ), support vector machine (Yuan et al. 2010 ; Bobra Couvidat 2015 ; Nishizuka et al. 2017 ; Sadykov Kosovichev 2017 ), random forests (Liu et al. 2017 ; Florios et al. 2018 ), and ensemble learning (Colak Qahwaji 2009 ; Huang et al. 2010 ; Guerra et al. 2015 ). Because flares originate from the magnetic field around sunspots in an active region (AR), many of the above studies utilize photospheric magnetic observational data of AR for binary class prediction. Both statistical methods and traditional machine-learning algorithms learn from features that are manually selected and extracted from the observational data. The predictive performance of these methods mainly relies on the quality of the features used. Deep learning neural networks (DNNs), which are as a new branch of machine learning, have become a highly reliable technique for solving large-scale learning problems in astronomy and other branches of science (Abraham et al. 2018 ). A famous method of DNNs, convolutional neural networks (CNNs; LeCun et al. 2015 ), have become very popular in the area of image processing and computer vision. It could learn directly from the raw data instead of features. CNNs are usually composed of several convolutional layers, and each convolutional layer is trained to automatically select and extract increasingly complex features from input data. There are some literatures reporting that CNNs have been successfully applied to solar flare prediction. Park et al. ( 2018 ) applied a CNN model to predict a binary class flare within 24 hr, which is based on a combination of GoogLeNet (Szegedy et al. 2014 ) and DenseNet (Huang et al. 2016 ). After trained by full-disk solar line of sight (LOS) magnetograms, their model could only make binary class prediction for a ≥C-class flare, which lacked research on the prediction for a ≥M-class major flare. Huang et al. ( 2018 ) presented a CNN model to predict a binary class flare, which is based on the classic CNN model architecture including two convolutional layers with 64 11