Computes the singular value decompositions of one or more matrices.
tf.raw_ops.Svd(
input, compute_uv=True, full_matrices=False, name=None
)
Computes the SVD of each inner matrix in input
such that
input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])
# a is a tensor containing a batch of matrices.
# s is a tensor of singular values for each matrix.
# u is the tensor containing the left singular vectors for each matrix.
# v is the tensor containing the right singular vectors for each matrix.
s, u, v = svd(a)
s, _, _ = svd(a, compute_uv=False)
Args | |
---|---|
input
|
A Tensor . Must be one of the following types: float64 , float32 , half , complex64 , complex128 .
A tensor of shape [..., M, N] whose inner-most 2 dimensions
form matrices of size [M, N] . Let P be the minimum of M and N .
|
compute_uv
|
An optional bool . Defaults to True .
If true, left and right singular vectors will be
computed and returned in u and v , respectively.
If false, u and v are not set and should never referenced.
|
full_matrices
|
An optional bool . Defaults to False .
If true, compute full-sized u and v . If false
(the default), compute only the leading P singular vectors.
Ignored if compute_uv is False .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A tuple of Tensor objects (s, u, v).
|
|
s
|
A Tensor . Has the same type as input .
|
u
|
A Tensor . Has the same type as input .
|
v
|
A Tensor . Has the same type as input .
|