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)
| 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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/Svd