vmlrelud - ReLU activation (first derivative)

4.6 vmlrelud - ReLU activation (first derivative)

subroutine vmlrelud (n, y, incy, x, incx); 
subroutine vmlreludf(n, y, incy, x, incx); 
 
subroutine vmlrelud_64 (n, y, incy, x, incx); 
subroutine vmlreludf_64(n, y, incy, x, incx);

Given an input vector $x$ and a result vector $y$ this function computes the first derivative of the ReLU activation function of the values in the $x$ vector and stores the result in the $y$ vector.

{ y .= 0 where x< 0∨ x= 0 1 where x> 0

The case where $x =0$ is normally undefined, for proper behavior in the application scenario of machine learning this function substitutes $0$ as a result on those elements to handle this condition gracefully.

4.6.1 Parameters

N - INTEGER: ENTRY: Number of elements of $x$ and $y$ .
CONSTRAINT: $n ≥1$ .
Y - ARRAY OF REAL: EXIT: Result vector $y$ .
CONSTRAINT: Must contain $n× incy$ elements.
CONSTRAINT: Must not overlap with array $x$ .
INCY - INTEGER: ENTRY: Stride for the vector $y$ .
CONSTRAINT: $incy̸= 0$ .
BEHAVIOR: A negative stride will traverse the array in reverse.
X - ARRAY OF REAL: ENTRY: Input vector $x$ .
CONSTRAINT: Must contain $n× incx$ elements.
CONSTRAINT: Must not overlap with array $y$ .
INCX - INTEGER: ENTRY: Stride for the vector $x$ .
CONSTRAINT: $incx̸= 0$ .
BEHAVIOR: A negative stride will traverse the array in reverse.

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