.. _op_ai_onnx_MeanVarianceNormalization:

MeanVarianceNormalization
=========================

- **Domain**: ``ai.onnx``
- **Since version**: 13

A MeanVarianceNormalization Function: Perform mean variance normalization
on the input tensor X using formula: ``(X-EX)/sqrt(E(X-EX)^2)``

**Inputs**

- **X** (*T*): Input tensor

**Outputs**

- **Y** (*T*): Output tensor

**Attributes**

- **axes** (*int[]*): A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.

**Type Constraints**

- **T**: Constrain input and output types to all numeric tensors.
  Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).

Examples
--------

**test_cc_mvn**

.. code-block:: text

    Node:
      MeanVarianceNormalization(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(3, 3, 3, 1, 2), dtype=float32
        [[[[[-1. ,  0. ]],
        
           [[ 0.5,  1. ]],
        
           [[ 2. ,  3. ]]],
        
        
          [[[ 4. ,  5. ]],
        
           [[ 6. ,  7. ]],
        
           [[ 8. ,  9. ]]],
        
        
          [[[10. , 11. ]],
        
           [[12. , 13. ]],
        
           [[14. , 15. ]]]],
        
        
        
         [[[[16. , 17. ]],
        
           [[18. , 19. ]],
        
           [[20. , 21. ]]],
        
        
          [[[22. , 23. ]],
        
           [[24. , 25. ]],
        
           [[26. , 27. ]]],
        
        
          [[[28. , 29. ]],
        
           [[30. , 31. ]],
        
           [[32. , 33. ]]]],
        
        
        
         [[[[34. , 35. ]],
        
           [[36. , 37. ]],
        
           [[38. , 39. ]]],
        
        
          [[[40. , 41. ]],
        
           [[42. , 43. ]],
        
           [[44. , 45. ]]],
        
        
          [[[46. , 47. ]],
        
           [[48. , 49. ]],
        
           [[50. , 51. ]]]]]

    Outputs:
      y: shape=(3, 3, 3, 1, 2), dtype=float32
        [[[[[-1.3153384 , -1.3054299 ]],
        
           [[-1.2123989 , -1.2371227 ]],
        
           [[-1.1094594 , -1.1005079 ]]],
        
        
          [[[-1.3525045 , -1.3525045 ]],
        
           [[-1.217254  , -1.217254  ]],
        
           [[-1.0820036 , -1.0820036 ]]],
        
        
          [[[-1.3525045 , -1.3525045 ]],
        
           [[-1.217254  , -1.217254  ]],
        
           [[-1.0820036 , -1.0820036 ]]]],
        
        
        
         [[[[-0.14869043, -0.14420448]],
        
           [[-0.01143773, -0.00758971]],
        
           [[ 0.12581497,  0.12902506]]],
        
        
          [[[-0.13525045, -0.13525045]],
        
           [[ 0.        ,  0.        ]],
        
           [[ 0.13525045,  0.13525045]]],
        
        
          [[[-0.13525045, -0.13525045]],
        
           [[ 0.        ,  0.        ]],
        
           [[ 0.13525045,  0.13525045]]]],
        
        
        
         [[[[ 1.0865839 ,  1.0853285 ]],
        
           [[ 1.2238367 ,  1.2219431 ]],
        
           [[ 1.3610893 ,  1.3585579 ]]],
        
        
          [[[ 1.0820036 ,  1.0820036 ]],
        
           [[ 1.217254  ,  1.217254  ]],
        
           [[ 1.3525045 ,  1.3525045 ]]],
        
        
          [[[ 1.0820036 ,  1.0820036 ]],
        
           [[ 1.217254  ,  1.217254  ]],
        
           [[ 1.3525045 ,  1.3525045 ]]]]]

**test_cc_mvn_explicit_axes**

.. code-block:: text

    Node:
      MeanVarianceNormalization(x) -> (y)
      Attributes:
        axes = [0, 2, 3]

.. code-block:: text

    Inputs:
      x: shape=(3, 3, 3, 1, 2), dtype=float32
        [[[[[-2., -1.]],
        
           [[ 0.,  1.]],
        
           [[ 2.,  3.]]],
        
        
          [[[ 4.,  5.]],
        
           [[ 6.,  7.]],
        
           [[ 8.,  9.]]],
        
        
          [[[10., 11.]],
        
           [[12., 13.]],
        
           [[14., 15.]]]],
        
        
        
         [[[[16., 17.]],
        
           [[18., 19.]],
        
           [[20., 21.]]],
        
        
          [[[22., 23.]],
        
           [[24., 25.]],
        
           [[26., 27.]]],
        
        
          [[[28., 29.]],
        
           [[30., 31.]],
        
           [[32., 33.]]]],
        
        
        
         [[[[34., 35.]],
        
           [[36., 37.]],
        
           [[38., 39.]]],
        
        
          [[[40., 41.]],
        
           [[42., 43.]],
        
           [[44., 45.]]],
        
        
          [[[46., 47.]],
        
           [[48., 49.]],
        
           [[50., 51.]]]]]

    Outputs:
      y: shape=(3, 3, 3, 1, 2), dtype=float32
        [[[[[-1.3525045 , -1.3525045 ]],
        
           [[-1.217254  , -1.217254  ]],
        
           [[-1.0820036 , -1.0820036 ]]],
        
        
          [[[-1.3525045 , -1.3525045 ]],
        
           [[-1.217254  , -1.217254  ]],
        
           [[-1.0820036 , -1.0820036 ]]],
        
        
          [[[-1.3525045 , -1.3525045 ]],
        
           [[-1.217254  , -1.217254  ]],
        
           [[-1.0820036 , -1.0820036 ]]]],
        
        
        
         [[[[-0.13525045, -0.13525045]],
        
           [[ 0.        ,  0.        ]],
        
           [[ 0.13525045,  0.13525045]]],
        
        
          [[[-0.13525045, -0.13525045]],
        
           [[ 0.        ,  0.        ]],
        
           [[ 0.13525045,  0.13525045]]],
        
        
          [[[-0.13525045, -0.13525045]],
        
           [[ 0.        ,  0.        ]],
        
           [[ 0.13525045,  0.13525045]]]],
        
        
        
         [[[[ 1.0820036 ,  1.0820036 ]],
        
           [[ 1.217254  ,  1.217254  ]],
        
           [[ 1.3525045 ,  1.3525045 ]]],
        
        
          [[[ 1.0820036 ,  1.0820036 ]],
        
           [[ 1.217254  ,  1.217254  ]],
        
           [[ 1.3525045 ,  1.3525045 ]]],
        
        
          [[[ 1.0820036 ,  1.0820036 ]],
        
           [[ 1.217254  ,  1.217254  ]],
        
           [[ 1.3525045 ,  1.3525045 ]]]]]

Differences with previous version (9)
-------------------------------------

**SchemaDiff**: ``MeanVarianceNormalization`` (domain ``'ai.onnx'``)

* old version: 9
* new version: 13
* breaking: no

**Type constraints:**

* changed 'T': added types: ['tensor(bfloat16)']

Version History
---------------

- :doc:`Version 9 <MeanVarianceNormalization-9>`