The normalized descriptor matrices were fed into a convolution layer with kernel size of 16×N×4 (batch × height × weight) and Relu activation, where N is the number of rows of the descriptor matrices. The output of the convolution layer was then flattened and expanded into a one-dimensional vector as inputs to the feed-forward neural network. The feed-forward neural network contained two hidden layers with 256 and 128 nodes respectively.