Additional file 3 of Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

Additional file 3. Python codes used to fit the deep learning (FFNN & CNN) algorithms to the simulated (animal breeding) dataset. Includes six py and three pnz files: three of the py files refer to the FFNN fits and the other three to the CNN fits; each of the three pnz files include six npy files referring to the training of the FFNNs for traits 1, 2 & 3, respectively.

附加文件3。本文件包含用于将深度学习算法（包括前馈神经网络（Feedforward Neural Network，FFNN）与卷积神经网络（Convolutional Neural Network，CNN））拟合至模拟动物育种数据集的Python代码。文件内含6个.py格式文件与3个.pnz格式文件：其中3个.py文件用于前馈神经网络的拟合任务，剩余3个用于卷积神经网络的拟合任务；3个.pnz格式文件各自包含6个.npy格式文件，分别对应性状1、性状2与性状3的前馈神经网络训练流程。