tf.raw_ops.Svd

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)

inputA 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_uvAn 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_matricesAn 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.
nameA name for the operation (optional).

A tuple of Tensor objects (s, u, v).
sA Tensor. Has the same type as input.
uA Tensor. Has the same type as input.
vA Tensor. Has the same type as input.