def svd(tensor, full_matrices=False, compute_uv=True, name=None):
"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :,
:])`
```python
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
tensor: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`. The values are sorted in reverse
order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the
second largest, etc.
u: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.svd, except that the order of output
arguments here is `s`, `u`, `v` when `compute_uv` is `True`, as opposed to
`u`, `s`, `v` for numpy.linalg.svd.
@end_compatibility
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(tensor,
compute_uv=compute_uv,
full_matrices=full_matrices,
name=name)
# pylint: enable=protected-access
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)
def svd(tensor, full_matrices=False, compute_uv=True, name=None):
"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :,
:])`
```prettyprint
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
tensor: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`. The values are sorted in reverse
order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the
second largest, etc.
u: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.svd, except that the order of output
arguments here is `s`, `u`, `v` when `compute_uv` is `True`, as opposed to
`u`, `s`, `v` for numpy.linalg.svd.
@end_compatibility
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(
tensor, compute_uv=compute_uv, full_matrices=full_matrices)
# pylint: enable=protected-access
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)
def svd(tensor, full_matrices=False, compute_uv=True, name=None):
"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :,
:])`
```prettyprint
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
#v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
tensor: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`.
u: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(tensor,
compute_uv=compute_uv,
full_matrices=full_matrices)
# pylint: enable=protected-access
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)
def svd(tensor, full_matrices=False, compute_uv=True, name=None):
"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :,
:])`
```prettyprint
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
matrix: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`.
u: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(
tensor, compute_uv=compute_uv, full_matrices=full_matrices)
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)
def svd(tensor, full_matrices=False, compute_uv=True, name=None):
r"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) *
transpose(conj(v[..., :, :]))`
```python
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
tensor: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`. The values are sorted in reverse
order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the
second largest, etc.
u: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.svd, except that
* The order of output arguments here is `s`, `u`, `v` when `compute_uv` is
`True`, as opposed to `u`, `s`, `v` for numpy.linalg.svd.
* full_matrices is `False` by default as opposed to `True` for
numpy.linalg.svd.
* tf.linalg.svd uses the standard definition of the SVD
\\(A = U \Sigma V^H\\), such that the left singular vectors of `a` are
the columns of `u`, while the right singular vectors of `a` are the
columns of `v`. On the other hand, numpy.linalg.svd returns the adjoint
\\(V^H\\) as the third output argument.
```python
import tensorflow as tf
s, u, v = tf.linalg.svd(a)
tf_a_approx = tf.matmul(u, tf.matmul(tf.linalg.diag(s), v, adjoint_v=True))
u, s, v_adj = np.linalg.svd(a, full_matrices=False)
np_a_approx = np.dot(u, np.dot(np.diag(s), v_adj))
# tf_a_approx and np_a_approx should be numerically close.
````
@end_compatibility
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(
tensor, compute_uv=compute_uv, full_matrices=full_matrices, name=name)
# pylint: enable=protected-access
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)
def svd(tensor, full_matrices=False, compute_uv=True, name=None):
r"""Computes the singular value decompositions of one or more matrices.
Computes the SVD of each inner matrix in `tensor` such that
`tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) *
transpose(conj(v[..., :, :]))`
```python
# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
```
Args:
tensor: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
`N`.
full_matrices: 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`.
compute_uv: If `True` then left and right singular vectors will be
computed and returned in `u` and `v`, respectively. Otherwise, only the
singular values will be computed, which can be significantly faster.
name: string, optional name of the operation.
Returns:
s: Singular values. Shape is `[..., P]`. The values are sorted in reverse
order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the
second largest, etc.
u: Left singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
`[..., M, M]`. Not returned if `compute_uv` is `False`.
v: Right singular vectors. If `full_matrices` is `False` (default) then
shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
`[..., N, N]`. Not returned if `compute_uv` is `False`.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.svd, except that
* The order of output arguments here is `s`, `u`, `v` when `compute_uv` is
`True`, as opposed to `u`, `s`, `v` for numpy.linalg.svd.
* full_matrices is `False` by default as opposed to `True` for
numpy.linalg.svd.
* tf.linalg.svd uses the standard definition of the SVD
\\(A = U \Sigma V^H\\), such that the left singular vectors of `a` are
the columns of `u`, while the right singular vectors of `a` are the
columns of `v`. On the other hand, numpy.linalg.svd returns the adjoint
\\(V^H\\) as the third output argument.
```python
import tensorflow as tf
import numpy as np
s, u, v = tf.linalg.svd(a)
tf_a_approx = tf.matmul(u, tf.matmul(tf.linalg.diag(s), v, adjoint_v=True))
u, s, v_adj = np.linalg.svd(a, full_matrices=False)
np_a_approx = np.dot(u, np.dot(np.diag(s), v_adj))
# tf_a_approx and np_a_approx should be numerically close.
````
@end_compatibility
"""
# pylint: disable=protected-access
s, u, v = gen_linalg_ops._svd(
tensor, compute_uv=compute_uv, full_matrices=full_matrices, name=name)
# pylint: enable=protected-access
if compute_uv:
return math_ops.real(s), u, v
else:
return math_ops.real(s)