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 | |
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`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 | |
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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` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/Svd