ac53316f49
* update spec * create op_reference test * add greateEqual to constants.py * add ngraph_check for number of inputs and outputs * add include * fix style * add information about broadcast in examples in spec * Remove redundant reegistration of a new test class * Remove visit_attributes method * update outputs part in Greater and GreaterEqual spec * fix typos in spec
2.6 KiB
2.6 KiB
Greater
Versioned name: Greater-1
Category: Comparison binary
Short description: Greater performs element-wise comparison operation with two given tensors applying broadcast rules specified in the auto_broadcast 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, Greater does the following with the input tensors a and b:
\f[ o_{i} = a_{i} > b_{i} \f]
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, description is available in Broadcast Rules For Elementwise Operations,
- pdpd - PaddlePaddle-style implicit broadcasting, description is available in Broadcast Rules For Elementwise Operations.
- Type: string
- Default value: "numpy"
- Required: no
Inputs
- 1: A tensor of type T and arbitrary shape. Required.
- 2: A tensor of type T and arbitrary shape. Required.
Outputs
- 1: The result of element-wise Greater operation applied to the input tensors. A tensor of type T_BOOL and shape equal to broadcasted shape of two inputs.
Types
- T: arbitrary supported type.
- T_BOOL:
boolean.
Examples
Example 1: no broadcast
<layer ... type="Greater">
<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: numpy broadcast
<layer ... type="Greater">
<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>