.. _op_ai_onnx_Shrink:

Shrink
======

- **Domain**: ``ai.onnx``
- **Since version**: 9

Shrink takes one input data (Tensor ) and produces one Tensor output,
having same datatype and shape with input. It has two attributes, lambd and
bias. The formula of this operator is: If x   lambd, y = x - bias; Otherwise, y = 0.

**Inputs**

- **input** (*T*): The input data as Tensor.

**Outputs**

- **output** (*T*): The output.

**Attributes**

- **bias** (*float*): The bias value added to output. Default is 0.
- **lambd** (*float*): The lambd value for the Shrink formulation. Default is 0.5.

**Type Constraints**

- **T**: Constrain input to only numeric types.
  Allowed types: tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).

Examples
--------

**test_cc_shrink_default**

.. code-block:: text

    Node:
      Shrink(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=float32
        [[-1. , -0.5, -0.1],
         [ 0.1,  0.5,  1. ]]

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

**test_cc_shrink_hard**

.. code-block:: text

    Node:
      Shrink(x) -> (y)
      Attributes:
        lambd = 1.5

.. code-block:: text

    Inputs:
      x: shape=(5,), dtype=float32
        [-2., -1.,  0.,  1.,  2.]

    Outputs:
      y: shape=(5,), dtype=float32
        [-2.,  0.,  0.,  0.,  2.]

**test_cc_shrink_soft**

.. code-block:: text

    Node:
      Shrink(x) -> (y)
      Attributes:
        bias = 1.5
        lambd = 1.5

.. code-block:: text

    Inputs:
      x: shape=(5,), dtype=float32
        [-2., -1.,  0.,  1.,  2.]

    Outputs:
      y: shape=(5,), dtype=float32
        [-0.5,  0. ,  0. ,  0. ,  0.5]