Maciej Smyk
0148076ed7
* code-block-1 * Update Convert_Model_From_Paddle.md * code-block force * fix * fix-2 * Update troubleshooting-steps.md * code-block-2 * Update README.md
113 lines
2.8 KiB
Markdown
113 lines
2.8 KiB
Markdown
# Maximum {#openvino_docs_ops_arithmetic_Maximum_1}
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@sphinxdirective
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.. meta::
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:description: Learn about Maximum-1 - an element-wise, arithmetic operation, which
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can be performed on a single tensor in OpenVINO.
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**Versioned name**: *Maximum-1*
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**Category**: *Arithmetic binary*
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**Short description**: *Maximum* performs element-wise maximum operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute.
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**Detailed description**
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As a first step input tensors *a* and *b* are broadcasted if their shapes differ. Broadcasting is performed according to ``auto_broadcast`` attribute specification. As a second step *Maximum* operation is computed element-wise on the input tensors *a* and *b* according to the formula below:
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After broadcasting *Maximum* does the following with the input tensors *a* and *b*:
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.. math::
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o_{i} = max(a_{i},\ b_{i})
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**Attributes**:
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* *auto_broadcast*
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* **Description**: specifies rules used for auto-broadcasting of input tensors.
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* **Range of values**:
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* *none* - no auto-broadcasting is allowed, all input shapes must match
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* *numpy* - numpy broadcasting rules, description is available in :doc:`Broadcast Rules For Elementwise Operations <openvino_docs_ops_broadcast_rules>`
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* **Type**: string
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* **Default value**: "numpy"
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* **Required**: *no*
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**Inputs**
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* **1**: A tensor of type *T* and arbitrary shape. **Required.**
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* **2**: A tensor of type *T* and arbitrary shape. **Required.**
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**Outputs**
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* **1**: The result of element-wise maximum operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
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**Types**
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* *T*: any numeric type.
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**Examples**
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*Example 1 - no broadcasting*
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.. code-block:: xml
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:force:
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<layer ... type="Maximum">
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<data auto_broadcast="none"/>
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<input>
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<port id="0">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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<port id="1">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</output>
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</layer>
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*Example 2: numpy broadcasting*
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.. code-block:: xml
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:force:
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<layer ... type="Maximum">
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<data auto_broadcast="numpy"/>
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<input>
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<port id="0">
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<dim>8</dim>
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<dim>1</dim>
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<dim>6</dim>
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<dim>1</dim>
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</port>
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<port id="1">
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<dim>7</dim>
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<dim>1</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>8</dim>
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<dim>7</dim>
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<dim>6</dim>
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<dim>5</dim>
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</port>
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</output>
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</layer>
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@endsphinxdirective
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