Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Returns element-wise squared value of the input... math::   square(x) = x^2Example::   square([2, 3, 4]) = [4, 9, 16]The storage type of ``square`` output depends upon the input storage type:   - square(default) = default   - square(row_sparse) = row_sparse   - square(csr) = csrDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L444
/// </summary>
/// <param name="@out">output NDArray</param>
/// <param name="data">The input array.</param>
/// <returns>returns new symbol</returns>
Returns element-wise squared value of the input...
Returns element - wise squared value of the input ...
public static NDArray Square(NDArray @out,
        NDArray data)
        {
            return new Operator("square")
            .SetInput("data", data)
            .Invoke(@out);
        }
public static NDArray Square ( NDArray @out , NDArray data ) { return new Operator ( " " ) . SetInput ( " " , data ) . Invoke ( @out ) ; }
Returns element-wise squared value of the input... math::   square(x) = x^2Example::   square([2, 3, 4]) = [4, 9, 16]The storage type of ``square`` output depends upon the input storage type:   - square(default) = default   - square(row_sparse) = row_sparse   - square(csr) = csrDefined in G:\deeplearn\mxnet\src\operator\tensor\elemwise_unary_op.cc:L444
Returns element - wise squared value of the input ... math :: square ( x ) = x^2Example :: square ( [ 2 3 4 ] ) = [ 4 9 16 ] The storage type of ` ` square ` ` output depends upon the input storage type : - square ( default ) = default - square ( row_sparse ) = row_sparse - square ( csr ) = csrDefined in G : \ deeplearn \ mxnet \ src \ operator \ tensor \ elemwise_unary_op . cc : L444
returns new symbol
returns new symbol
@out
NDArray
None
data
NDArray
The input array.
The input array .