Fig. 6From: Genomic benchmarks: a collection of datasets for genomic sequence classificationCNN architecture. The neural network consists of three convolutional layers with 16, 8, and 4 filters, with a kernel size of 8. The output of each convolutional layer goes through the batch normalization layer and the max-pooling layer. The output is then flattened and passes through two dense layers. The last layer is designed to predict the probabilities that the input sample belongs to any of the given classesBack to article page