Revise ReLU (#2863)

* remove relu_backprop

* Update ReLU spec

* change inputs and outputs subsections of ReLU spec

* Update Mathematical Formulation subsection

* Update Category of ReLU in spec

* Update Short description of ReLU in spec

docs/ops/activation/ReLU_1.md

@@ -2,9 +2,9 @@
 
 **Versioned name**: *ReLU-1*
 
-**Category**: *Activation*
+**Category**: *Activation function*
 
-**Short description**: [Reference](http://caffe.berkeleyvision.org/tutorial/layers/relu.html)
+**Short description**: ReLU element-wise activation function. ([Reference](http://caffe.berkeleyvision.org/tutorial/layers/relu.html))
 
 **Detailed description**: [Reference](https://github.com/Kulbear/deep-learning-nano-foundation/wiki/ReLU-and-Softmax-Activation-Functions#rectified-linear-units)
 
@@ -12,13 +12,19 @@
 
 **Mathematical Formulation**
 
-\f[
-Y_{i}^{( l )} = max(0, Y_{i}^{( l - 1 )})
-\f]
+For each element from the input tensor calculates corresponding
+ element in the output tensor with the following formula:
+ \f[
+ Y_{i}^{( l )} = max(0, Y_{i}^{( l - 1 )})
+ \f]
 
 **Inputs**:
 
-*   **1**: Multidimensional input tensor. Required.
+*   **1**: Multidimensional input tensor *x* of any supported numeric type. Required.
+
+**Outputs**:
+
+*   **1**: Result of ReLU function applied to the input tensor *x*. Tensor with shape and type matching the input tensor. Required.
 
 **Example**
 

ngraph/core/reference/include/ngraph/runtime/reference/relu.hpp

@@ -33,15 +33,6 @@ namespace ngraph
                     out[i] = arg[i] > zero ? arg[i] : zero;
                 }
             }
-            template <typename T>
-            void relu_backprop(const T* arg, const T* delta_arg, T* out, size_t count)
-            {
-                T zero = 0;
-                for (size_t i = 0; i < count; i++)
-                {
-                    out[i] = arg[i] > zero ? delta_arg[i] : zero;
-                }
-            }
         }
     }
 }