**Inputs**
-* **1**: Input tensor x of any data type that has defined *logical and* operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with *logical and* operation along dimensions specified by the 2nd input:
- output[i0, i1, ..., iN] = and[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = and[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and *logical and* operation `and[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalAnd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalAnd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalAnd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
**Inputs**
-* **1**: Input tensor x of any data type that has defined *logical or* operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with *logical or* operation along dimensions specified by the 2nd input:
Where indices i0, ..., iN run through all valid indices for the 1st input and *logical or* operation `or[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalOr" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalOr" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceLogicalOr" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
-## ReduceLp <a name="ReduceLp"></a>
+## ReduceLp <a name="ReduceLp"></a> {#openvino_docs_ops_reduction_ReduceLp_4}
**Versioned name**: *ReduceLp-4*
**Inputs**
-* **1**: Input tensor x of any data type that has defined maximum operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+** Types **
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with finding a maximum operation along dimensions specified by the 2nd input:
- output[i0, i1, ..., iN] = max[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = max[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and finding the maximum value `max[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMax" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMax" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMax" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
**Inputs**
-* **1**: Input tensor x of any data type that has defined the arithmetic mean operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with finding the arithmetic mean operation along dimensions specified by the 2nd input:
-
- output[i0, i1, ..., iN] = mean[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = mean[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and finding the arithmetic mean `mean[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMean" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMean" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMean" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
**Inputs**
-* **1**: Input tensor x of any data type that has defined minimum operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with finding a minimum operation along dimensions specified by the 2nd input:
- output[i0, i1, ..., iN] = min[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = min[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and finding the minimum value `min[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMin" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMin" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceMin" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
**Inputs**
-* **1**: Input tensor x of any data type that has defined multiplication operation. **Required.**
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required.**
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with multiplication operation along dimensions specified by the 2nd input:
- output[i0, i1, ..., iN] = prod[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = prod[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and multiplication `prod[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceProd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceProd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceProd" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
**Inputs**
-* **1**: Input tensor x of any data type that has defined addition operation. **Required**.
+* **1**: Input tensor x of type *T1*. **Required.**
-* **2**: Scalar or 1D tensor with axis indices for the 1st input along which reduction is performed. **Required**.
+* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r-1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
+**Types**
+
+* *T1*: any supported numeric type.
+* *T_IND*: `int64` or `int32`.
+
**Detailed Description**
Each element in the output is the result of reduction with addition operation along dimensions specified by the 2nd input:
- output[i0, i1, ..., iN] = sum[j0,..., jN](x[j0, ..., jN]**2))
+ output[i0, i1, ..., iN] = sum[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and summation `sum[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- 1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- 2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
+
+1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
+2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction value is calculated for entire input tensor.
**Example**
</port>
</output>
</layer>
+```
+
+```xml
+<layer id="1" type="ReduceSum" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceSum" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
+```
+
+```xml
+<layer id="1" type="ReduceSum" ...>
+ <data keep_dims="False" />
+ <input>
+ <port id="0">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>10</dim>
+ <dim>24</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
+ </port>
+ </input>
+ <output>
+ <port id="2">
+ <dim>6</dim>
+ <dim>12</dim>
+ <dim>24</dim>
+ </port>
+ </output>
+</layer>
```
\ No newline at end of file
projects / platform / upstream / dldt.git / commitdiff