openvino/docs/ops/arithmetic/Subtract_1.md

# Subtract {#openvino_docs_ops_arithmetic_Subtract_1}

@sphinxdirective

**Versioned name**: *Subtract-1*

**Category**: *Arithmetic binary*

**Short description**: *Subtract* performs element-wise subtraction operation with two given tensors applying broadcasting rule specified in the *auto_broacast* attribute.

**Detailed description**
Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and ``auto_broadcast`` attribute is not ``none``. Broadcasting is performed according to ``auto_broadcast`` value.
After broadcasting *Subtract* performs subtraction operation for the input tensors *a* and *b* using the formula below:

.. math::

   o_{i} = a_{i} - b_{i}


**Attributes**:

* *auto_broadcast*

  * **Description**: specifies rules used for auto-broadcasting of input tensors.
  * **Range of values**:
  
    * *none* - no auto-broadcasting is allowed, all input shapes must match,
    * *numpy* - numpy broadcasting rules, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`, 
    * *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`.
  * **Type**: string
  * **Default value**: "numpy"
  * **Required**: *no*

**Inputs**

* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.**
* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.**

**Outputs**

* **1**: The result of element-wise subtraction operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs.

**Types**

* *T*: any numeric type.

**Examples**

*Example 1*

.. code-block:: cpp 

    <layer ... type="Subtract">
        <data auto_broadcast="none"/>
        <input>
            <port id="0">
                <dim>256</dim>
                <dim>56</dim>
            </port>
            <port id="1">
                <dim>256</dim>
                <dim>56</dim>
            </port>
        </input>
        <output>
            <port id="2">
                <dim>256</dim>
                <dim>56</dim>
            </port>
        </output>
    </layer>

*Example 2: broadcast*

.. code-block:: cpp 

    <layer ... type="Subtract">
        <data auto_broadcast="numpy"/>
        <input>
            <port id="0">
                <dim>8</dim>
                <dim>1</dim>
                <dim>6</dim>
                <dim>1</dim>
            </port>
            <port id="1">
                <dim>7</dim>
                <dim>1</dim>
                <dim>5</dim>
            </port>
        </input>
        <output>
            <port id="2">
                <dim>8</dim>
                <dim>7</dim>
                <dim>6</dim>
                <dim>5</dim>
            </port>
        </output>
    </layer>


@endsphinxdirective