.. _op_ai_onnx_Relu:

Relu
====

- **Domain**: ``ai.onnx``
- **Since version**: 14

Relu takes one input data (Tensor ) and produces one output data
(Tensor ) where the rectified linear function, y = max(0, x), is applied to
the tensor elementwise.

**Inputs**

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

**Outputs**

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

**Type Constraints**

- **T**: Constrain input and output types to signed numeric tensors.
  Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8).

Examples
--------

**test_cc_relu**

.. code-block:: text

    Node:
      Relu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float32
        [[-3., -1.,  0.],
         [ 1.,  2.,  3.]]

    Outputs:
      Y: shape=(2, 3), dtype=float32
        [[0., 0., 0.],
         [1., 2., 3.]]

**test_cc_relu_bfloat16**

.. code-block:: text

    Node:
      Relu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=bfloat16
        [[-2, -0.5, 0],
         [0.5, 1.5, 3]]

    Outputs:
      Y: shape=(2, 3), dtype=bfloat16
        [[0, 0, 0],
         [0.5, 1.5, 3]]

**test_cc_relu_double**

.. code-block:: text

    Node:
      Relu(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=float64
        [[-2. , -0.5,  0. ],
         [ 0.5,  1.5,  3. ]]

    Outputs:
      y: shape=(2, 3), dtype=float64
        [[0. , 0. , 0. ],
         [0.5, 1.5, 3. ]]

**test_cc_relu_example**

.. code-block:: text

    Node:
      Relu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(3, 4, 5), dtype=float32
        [[[-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.]],
        
         [[-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.]],
        
         [[-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.],
          [-1., -1., -1., -1., -1.]]]

    Outputs:
      Y: shape=(3, 4, 5), dtype=float32
        [[[0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.]],
        
         [[0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.]],
        
         [[0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.],
          [0., 0., 0., 0., 0.]]]

**test_cc_relu_float16**

.. code-block:: text

    Node:
      Relu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float16
        [[-3., -1.,  0.],
         [ 1.,  2.,  3.]]

    Outputs:
      Y: shape=(2, 3), dtype=float16
        [[0., 0., 0.],
         [1., 2., 3.]]

**test_cc_relu_int16**

.. code-block:: text

    Node:
      Relu(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=int16
        [[-500,   -1,    0],
         [   1,  300, 1000]]

    Outputs:
      y: shape=(2, 3), dtype=int16
        [[   0,    0,    0],
         [   1,  300, 1000]]

**test_cc_relu_int32**

.. code-block:: text

    Node:
      Relu(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=int32
        [[-100000,      -1,       0],
         [      1,      42,  100000]]

    Outputs:
      y: shape=(2, 3), dtype=int32
        [[     0,      0,      0],
         [     1,     42, 100000]]

**test_cc_relu_int64**

.. code-block:: text

    Node:
      Relu(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=int64
        [[-1000000000000,             -1,              0],
         [             1,             42,  1000000000000]]

    Outputs:
      y: shape=(2, 3), dtype=int64
        [[            0,             0,             0],
         [            1,            42, 1000000000000]]

**test_cc_relu_int8**

.. code-block:: text

    Node:
      Relu(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=int8
        [[ -5,  -1,   0],
         [  1,   3, 127]]

    Outputs:
      y: shape=(2, 3), dtype=int8
        [[  0,   0,   0],
         [  1,   3, 127]]

Differences with previous version (13)
--------------------------------------

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

* old version: 13
* new version: 14
* breaking: no

**Type constraints:**

* changed 'T': added types: ['tensor(int16)', 'tensor(int32)', 'tensor(int64)', 'tensor(int8)']

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

- :doc:`Version 13 <Relu-13>`
- :doc:`Version 6 <Relu-6>`
- :doc:`Version 1 <Relu-1>`