PReLU specification refactoring (#5016)

* Review spec of PReLU operation

* Address review comments

   * Correct second input description
   * Add note to clarify input channel dimension
   * Add additional equivalent formula for op
   * Change reference link to abstract
   * Add additional examples

* Address review comments related to wording

* Fix IR layer examples

docs/ops/activation/PReLU_1.md

@@ -2,32 +2,114 @@
 
 **Versioned name**: *PReLU-1*
 
-**Category**: Activation function
+**Category**: *Activation function*
 
-**Short description**: *PReLU* performs element-wise parametric ReLU operation with negative slope defined by the second input.
+**Short description**: Parametric rectified linear unit element-wise activation function.
 
-**Attributes**: operation has no attributes.
+**Detailed description**
+
+*PReLU* operation is introduced in this [article](https://arxiv.org/abs/1502.01852v1).
+
+*PReLU* performs element-wise parametric *ReLU* operation on a given input tensor, based on the following mathematical formula:
+
+\f[
+PReLU(x) = \left\{\begin{array}{r}
+    x \quad \mbox{if } x \geq  0 \\
+    \alpha x \quad \mbox{if } x < 0
+\end{array}\right.
+\f]
+
+where α is a learnable parameter and corresponds to the negative slope, per channel, defined by the second input `slope`.
+
+Another mathematical representation that may be found in other references:
+
+\f[
+PReLU(x) = \max(0, x) + \alpha\cdot\min(0, x)
+\f]
+
+
+**Attributes**: *PReLU* operation has no attributes.
 
 **Inputs**
 
-* **1**: `X` - Input tensor of any supported floating point type T1. Required.
-
-* **2**: `slope` - Tensor with negative slope values of type T2. The shape of the tensor should be broadcastable to input 1. Required.
+* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required**.
+* **2**: `slope`. 1D tensor of type `T`. Tensor with negative slope values, one per channel dimension of `data` input tensor. **Required**.
+* **Note**: Channels dimension corresponds to second dimension of `data` input tensor. If `data` rank is less than 2, the number of channels is 1.
 
 **Outputs**
 
-* **1**: The result of element-wise PReLU operation applied for tensor from input 1 with slope values from input 2. A tensor of type T1 and shape matching shape of input *x* tensor.
+* **1**: The result of element-wise *PReLU* operation applied to `data` input tensor with negative slope values from `slope` input tensor. A tensor of type `T` and the same shape as `data` input tensor.
 
 **Types**
 
-* *T1*: arbitrary supported floating point type.
+* *T*: arbitrary supported floating-point type.
 
-* *T2*: arbitrary supported floating point type.
+**Examples**
 
-**Detailed description**
-Before performing addition operation, input tensor 2 with slope values is broadcasted to input 1.
-The broadcasting rules are aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
+*Example: 1D input tensor `data`*
 
-After broadcasting *PReLU* does the following for each input 1 element x:
+```xml
+<layer ... type="Prelu">
+    <input>
+        <port id="0">
+            <dim>128</dim>
+        </port>
+        <port id="1">
+            <dim>1</dim>
+        </port>
+    </input>
+    <output>
+        <port id="2">
+            <dim>128</dim>
+        </port>
+    </output>
+</layer>
+```
 
-    f(x) = slope * x for x < 0; x for x >= 0
+*Example: 2D input tensor `data`*
+
+```xml
+<layer ... type="Prelu">
+    <input>
+        <port id="0">
+            <dim>20</dim>
+            <dim>128</dim>
+        </port>
+        <port id="1">
+            <dim>128</dim>
+        </port>
+    </input>
+    <output>
+        <port id="2">
+            <dim>20</dim>
+            <dim>128</dim>
+        </port>
+    </output>
+</layer>
+```
+
+*Example: 4D input tensor `data`*
+
+```xml
+<layer ... type="Prelu">
+    <input>
+        <port id="0">
+            <dim>1</dim>
+            <dim>20</dim>
+            <dim>128</dim>
+            <dim>128</dim>
+        </port>
+        <port id="1">
+            <dim>20</dim>
+        </port>
+    </input>
+    <output>
+        <port id="2">
+            <dim>1</dim>
+            <dim>20</dim>
+            <dim>128</dim>
+            <dim>128</dim>
+        </port>
+    </output>
+</layer>
+```