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openvino/docs/ops/arithmetic/Multiply_1.md

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## Multiply <a name="Multiply"></a> {#openvino_docs_ops_arithmetic_Multiply_1}
2020-06-19 14:39:57 +03:00
**Versioned name**: *Multiply-1*
**Category**: Arithmetic binary operation
**Short description**: *Multiply* performs element-wise multiplication operation with two given tensors applying multi-directional broadcast rules.
**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 should match
* *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
* **Type**: string
* **Default value**: "numpy"
* **Required**: *no*
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
**Outputs**
* **1**: The result of element-wise multiplication operation. A tensor of type T.
**Types**
* *T*: any numeric type.
**Detailed description**
Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
After broadcasting *Multiply* does the following with the input tensors *a* and *b*:
\f[
o_{i} = a_{i} * b_{i}
\f]
**Examples**
*Example 1*
```xml
<layer ... type="Multiply">
<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*
```xml
<layer ... type="Multiply">
<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>
```