An activation function is a mathematical tool used in machine learning to impart non-linearities into linear systems. Choosing the proper activation function(s) is an important step in designing your neural network. The IDLmlafGaussian activation function is implemented with the following formula:


Compile_opt idl2
Data = findgen(10)
actFunc = IDLmlafGaussian()
Print, actFunc(data)


actFunc = IDLmlafGaussian()

Result = actFunc(X [, GRADIENT=value])

Note: IDL activation functions work by overloading the function operator. To avoid compilation problems, make sure that every routine that uses an activation function contains the statement:

Compile_opt idl2




GRADIENT (optional)

Set this keyword to a named variable to receive the gradient of the activation function at each point in the input X array. The output gradient will have the same dimensions as the input X array.

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See Also

IDLmlafArcTan, IDLmlafBentIdentity, IDLmlafBinaryStep, IDLmlafELU, IDLmlafIdentity, IDLmlafISRLU, IDLmlafISRU, IDLmlafLogistic, IDLmlafPReLU, IDLmlafReLU, IDLmlafSinc, IDLmlafSinusoid, IDLmlafSoftExponential, IDLmlafSoftmax, IDLmlafSoftPlus, IDLmlafSoftSign, IDLmlafTanH