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120 lines
3.9 KiB
Markdown
120 lines
3.9 KiB
Markdown
# NormalizeL2 {#openvino_docs_ops_normalization_NormalizeL2_1}
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@sphinxdirective
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**Versioned name**: *NormalizeL2-1*
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**Category**: *Normalization*
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**Short description**: *NormalizeL2* operation performs L2 normalization on a given input ``data`` along dimensions specified by ``axes`` input.
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**Detailed Description**
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Each element in the output is the result of dividing the corresponding element of ``data`` input by the result of L2 reduction along dimensions specified by the ``axes`` input:
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.. code-block:: cpp
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output[i0, i1, ..., iN] = x[i0, i1, ..., iN] / sqrt(eps_mode(sum[j0,..., jN](x[j0, ..., jN]**2), eps))
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Where indices ``i0, ..., iN`` run through all valid indices for the ``data`` input and summation ``sum[j0, ..., jN]`` has ``jk = ik`` for those dimensions ``k`` that are not in the set of indices specified by the ``axes`` input of the operation.
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``eps_mode`` selects how the reduction value and ``eps`` are combined. It can be ``max`` or ``add`` depending on ``eps_mode`` attribute value.
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Particular cases:
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1. If ``axes`` is an empty list, then each input element is divided by itself resulting value ``1`` for all non-zero elements.
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2. If ``axes`` contains all dimensions of input ``data``, a single L2 reduction value is calculated for the entire input tensor and each input element is divided by that value.
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**Attributes**
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* *eps*
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* **Description**: *eps* is the number applied by *eps_mode* function to the sum of squares to avoid division by zero when normalizing the value.
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* **Range of values**: a positive floating-point number
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* **Type**: ``float``
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* **Required**: *yes*
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* *eps_mode*
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* **Description**: Specifies how *eps* is combined with the sum of squares to avoid division by zero.
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* **Range of values**: ``add`` or ``max``
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* **Type**: ``string``
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* **Required**: *yes*
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**Inputs**
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* **1**: ``data`` - A tensor of type *T* and arbitrary shape. **Required.**
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* **2**: ``axes`` - Axis indices of ``data`` input tensor, along which L2 reduction is calculated. A scalar or 1D tensor of unique elements and type *T_IND*. The range of elements is ``[-r, r-1]``, where ``r`` is the rank of ``data`` input tensor. **Required.**
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**Outputs**
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* **1**: The result of *NormalizeL2* function applied to ``data`` input tensor. Normalized tensor of the same type and shape as the data input.
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**Types**
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* *T*: arbitrary supported floating-point type.
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* *T_IND*: any supported integer type.
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**Examples**
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Example: Normalization over channel dimension for ``NCHW`` layout
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.. code-block:: cpp
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<layer id="1" type="NormalizeL2" ...>
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<data eps="1e-8" eps_mode="add"/>
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>1</dim> < !-- axes list [1] means normalization over channel dimension -->
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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>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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</output>
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</layer>
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Example: Normalization over channel and spatial dimensions for ``NCHW`` layout
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.. code-block:: cpp
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<layer id="1" type="NormalizeL2" ...>
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<data eps="1e-8" eps_mode="add"/>
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>3</dim> < !-- axes list [1, 2, 3] means normalization over channel and spatial dimensions -->
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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>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</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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