The GLU activation function is defined as: glu (x) = a * sigmoid (b), where x is split into two equal parts a and b along the given axis.

Applies the rectified linear unit activation function. With default values, this returns the standard ReLU activation: max(x, 0), the element-wise maximum of 0 and the input tensor.

Jul 26, 2025 · This paper investigates substituting the MLP and attention mechanism usually adopted for transformer architecture with an architecture based on incorporating a gated linear unit (GLU) …

Nov 14, 2025 · The Gated Linear Unit (GLU) is a powerful activation function that offers unique advantages in terms of non-linearity and gradient flow. In PyTorch, implementing GLU is …

The GLU activation function is defined as: where x is split into two equal parts a and b along the given axis. A tensor, the result from applying the activation to the input tensor x. Input tensor. The axis …

May 24, 2024 · One drawback to these architectures is their reliance on the scaled dot product attention mechanism with the softmax activation function, which is computationally expensive and requires …

Apr 4, 2025 · The Gated Linear Unit (GLU) function in Keras is a powerful activation function that can improve your neural network’s efficiency and accuracy.

Return the extra representation of the module. Runs the forward pass.

May 24, 2024 · This paper investigates substituting the attention mechanism usually adopted for transformer architecture with an architecture incorporating gated linear unit (GLU) activation within a...

This article explains Glu activation in clear, practical terms: what it is, why it matters, how to implement it, common pitfalls, and actionable takeaways you can use today.