基于双输入门控网络的多子损失自适应优化方法及系统
本发明提供一种基于双输入门控网络的多子损失自适应优化方法及系统,涉及人工智能技术领域。本发明通过融合本次训练的隐藏特征和历史训练统计量,使门控网络能够实时感知数据特性和训练状态,智能调整各子损失权重,也即通过双输入门控网络实现了多子损失权重的动态自适应分配,有效解决了固定损失函数导致的模型识别精度低的问题,如此使模型能够自动聚焦难样本和冲突任务,优化训练资源分配,提高了分类识别模型的识别精度。
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