# Which is the activation function of the Gelu?

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## Which is the activation function of the Gelu?

The Gaussian Error Linear Unit, or GELU, is an activation function. The GELU activation function is \$xPhi (x)\$, where \$Phi (x)\$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than gates inputs by their sign as in ReLUs (\$xmathbf {1}_ {x>0}\$).

## What is the equation for Gelu in Bert?

I was going through BERT paper which uses GELU (Gaussian Error Linear Unit) which states equation as GELU(x) = xP(X ≤ x) = xΦ(x). which in turn is approximated to 0.5x(1 + tanh[√2 / π(x + 0.044715×3)]) Could you simplify the equation and explain how it has been approximated.

## How does Gelu work with the RNN regularizer?

Both of the above methods together decide a neuron’s output. Yet, the two work independently from each other. GELU aims to combine them. Also, a new RNN regularizer called Zoneout stochastically multiplies the input by 1.

## How to combine the C tionalities in Gelu?

GELU aims to combine them. Also, a new RNN regularizer called Zoneout stochastically multiplies the input by 1. We want to merge all 3 fun c tionalities by stochastically multiplying the input by 0 or 1 and getting the output value (of the activation function) deterministically.

## Is there a new activation function called Gelu?

GELU activation. A new activation function called GELU… | by SG | Medium Activations like ReLU, ELU and PReLU have enabled faster and better convergence of Neural Networks than sigmoids. Also, Dropout regularizes the model by randomly multiplying a few activations by 0. Both of the above methods together decide a neuron’s output.

Both of the above methods together decide a neuron’s output. Yet, the two work independently from each other. GELU aims to combine them. Also, a new RNN regularizer called Zoneout stochastically multiplies the input by 1.

GELU aims to combine them. Also, a new RNN regularizer called Zoneout stochastically multiplies the input by 1. We want to merge all 3 fun c tionalities by stochastically multiplying the input by 0 or 1 and getting the output value (of the activation function) deterministically.