The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa ...
示例1 defsuggest_via_metalearning(meta_base,dataset_name,metric,task,sparse,num_initial_configurations):logger=get_logger('autosklearn.metalearning.mismbo')iftask==MULTILABEL_CLASSIFICATION:task=MULTICLASS_CLASSIFICATION task=TASK_TYPES_TO_STRING[task]logger.warning(task)start=time.time()ml=MetaLearn...
python的实现,抛去其他的不用看,我们看下Layer那行, 仔细看看,type="multi-class-cross-entropy",即classification_cost的实现是多类的交叉熵,我们直接来抠下c++的实现: classMultiClassCrossEntropy:publicCostLayer{public:explicitMultiClassCrossEntropy(constLayerConfig&config):CostLayer(config){}boolinit(constLay...
Graham B, El-nouby A, Joulin A, Touvron H (2021) LeViT : a Vision Transformer in ConvNet ’ s Clothing for Faster Inference arXiv : 2104 . 01136v2 [ cs . CV ] 6 May Hameed N, Shabut AM, Ghosh MK, Hossain MA (2020) Multi-class multi-level classification algorithm for skin lesi...
we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compar...
In multiclass classification problems, Macro_F1-score is used as an evaluation indicator [19]. For an N-classification problem, the calculation formula is 𝐹1=1𝑁∑𝑖=1𝑁𝐹1𝑖F1=1N∑i=1NF1i (38) where 𝐹1𝑖F1i represents the F1-score for the ith category. In addition...