Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Returns element-wise exponential value of the input... math::   exp(x) = e^x \approx 2.718^xExample::   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]The storage type of ``exp`` output is always denseDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L543
/// </summary>
/// <param name="@out">output NDArray</param>
/// <param name="data">The input array.</param>
/// <returns>returns new symbol</returns>
Returns element-wise exponential value of the input...
Returns element - wise exponential value of the input ...
public static NDArray Exp(NDArray @out,
        NDArray data)
        {
            return new Operator("exp")
            .SetInput("data", data)
            .Invoke(@out);
        }
public static NDArray Exp ( NDArray @out , NDArray data ) { return new Operator ( " " ) . SetInput ( " " , data ) . Invoke ( @out ) ; }
Returns element-wise exponential value of the input... math::   exp(x) = e^x \approx 2.718^xExample::   exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]The storage type of ``exp`` output is always denseDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L543
Returns element - wise exponential value of the input ... math :: exp ( x ) = e^x \ approx 2 . 718^xExample :: exp ( [ 0 1 2 ] ) = [ 1 . 2 . 71828175 7 . 38905621 ] The storage type of ` ` exp ` ` output is always denseDefined in G : \ deeplearn \ mxnet \ src \ operator \ tensor \ elemwise_unary_op . cc : L543
returns new symbol
returns new symbol
@out
NDArray
None
data
NDArray
The input array.
The input array .