在深度学习领域，模型的性能很大程度上取决于其架构的设计。近年来，一种新颖的激活函数脱颖而出，那就是Gated Linear Units（GLU）✨。与传统的ReLU或Sigmoid等激活函数不同，GLU通过引入门控机制来调节信息流，从而使得神经网络能够更有效地捕捉数据中的复杂模式和非线性关系🔍。
GLU的基本思想是将输入信号分成两部分，一部分用于计算一个sigmoid函数，作为“门”来决定哪些信息应该被传递；另一部分则直接通过，与门的输出相乘，最终得到该神经元的输出结果/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate Outputs/Gate 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