Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Returns element-wise inverse square-root value of the input... math::   rsqrt(x) = 1/\sqrt{x}Example::   rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25]The storage type of ``rsqrt`` output is always denseDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L487
/// </summary>
/// <param name="@out">output NDArray</param>
/// <param name="data">The input array.</param>
/// <returns>returns new symbol</returns>
Returns element-wise inverse square-root value of the input...
Returns element - wise inverse square - root value of the input ...
public static NDArray Rsqrt(NDArray @out,
        NDArray data)
        {
            return new Operator("rsqrt")
            .SetInput("data", data)
            .Invoke(@out);
        }
public static NDArray Rsqrt ( NDArray @out , NDArray data ) { return new Operator ( " " ) . SetInput ( " " , data ) . Invoke ( @out ) ; }
Returns element-wise inverse square-root value of the input... math::   rsqrt(x) = 1/\sqrt{x}Example::   rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25]The storage type of ``rsqrt`` output is always denseDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L487
Returns element - wise inverse square - root value of the input ... math :: rsqrt ( x ) = 1 / \ sqrt { x } Example :: rsqrt ( [ 4 9 16 ] ) = [ 0 . 5 0 . 33333334 0 . 25 ] The storage type of ` ` rsqrt ` ` output is always denseDefined in G : \ deeplearn \ mxnet \ src \ operator \ tensor \ elemwise_unary_op . cc : L487
returns new symbol
returns new symbol
@out
NDArray
None
data
NDArray
The input array.
The input array .