public final class
Svd
Computes the eigen decomposition of a batch of self-adjoint matrices
(Note: Only real inputs are supported).
Computes the eigenvalues and eigenvectors of the innermost M-by-N matrices in tensor such that tensor[...,:,:] = u[..., :, :] * Diag(s[..., :]) * Transpose(v[...,:,:]).
Constants
| String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
| static <T extends TType> Svd<T> | |
| Output<T> |
s()
Singular values.
|
| Output<T> |
u()
Left singular vectors.
|
| Output<T> |
v()
Right singular vectors.
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"XlaSvd"
Public Methods
public static Svd<T> create (Scope scope, Operand<T> a, Long maxIter, Float epsilon, String precisionConfig)
Factory method to create a class wrapping a new Svd operation.
Parameters
| scope | current scope |
|---|---|
| a | the input tensor. |
| maxIter | maximum number of sweep update, i.e., the whole lower triangular part or upper triangular part based on parameter lower. Heuristically, it has been argued that approximately log(min (M, N)) sweeps are needed in practice (Ref: Golub & van Loan "Matrix Computation"). |
| epsilon | the tolerance ratio. |
| precisionConfig | a serialized xla::PrecisionConfig proto. |
Returns
- a new instance of Svd
public Output<T> s ()
Singular values. The values are sorted in reverse order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the second largest, etc.