revise RNN spec (openvinotoolkit#8081)

* revise RNN spec

Signed-off-by: fishbell <[email protected]>

* formatting the formula

Signed-off-by: fishbell <[email protected]>

* Update docs/ops/sequence/RNNSequence_5.md

Co-authored-by: Tatiana Savina <[email protected]>

Co-authored-by: Tatiana Savina <[email protected]>

Author: songbell

docs/ops/sequence/RNNCell_3.md

@@ -6,6 +6,18 @@
 
 **Short description**: *RNNCell* represents a single RNN cell that computes the output using the formula described in the [article](https://hackernoon.com/understanding-architecture-of-lstm-cell-from-scratch-with-code-8da40f0b71f4).
 
+**Detailed description**:
+
+*RNNCell* represents a single RNN cell and is part of [RNNSequence](RNNSequence_5.md) operation.
+
+```
+Formula:
+  *  - matrix multiplication
+  ^T - matrix transpose
+  f  - activation function
+    Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+```
+
 **Attributes**
 
 * *hidden_size*
@@ -43,7 +55,7 @@
 
 * **1**: `X` - 2D tensor of type *T* `[batch_size, input_size]`, input data. **Required.**
 
-* **2**: `initial_hidden_state` - 2D tensor of type *T* `[batch_size, hidden_size]`. **Required.**
+* **2**: `H` - 2D tensor of type *T* `[batch_size, hidden_size]`, initial hidden state. **Required.**
 
 * **3**: `W` - 2D tensor tensor of type *T* `[hidden_size, input_size]`, the weights for matrix multiplication. **Required.**
 

docs/ops/sequence/RNNSequence_5.md

@@ -46,7 +46,7 @@ A single cell in the sequence is implemented in the same way as in <a href="#RNN
 
 * *direction*
 
-  * **Description**: Specify if the RNN is forward, reverse, or bidirectional. If it is one of *forward* or *reverse* then `num_directions = 1`, if it is *bidirectional*, then `num_directions = 2`. This `num_directions` value specifies input/output shape requirements.
+  * **Description**: Specify if the RNN is forward, reverse, or bidirectional. If it is one of *forward* or *reverse*, then `num_directions = 1`. If it is *bidirectional*, then `num_directions = 2`. This `num_directions` value specifies input/output shape requirements. When the operation is bidirectional, the input goes through forward and reverse ways. The outputs are concatenated.
   * **Range of values**: *forward*, *reverse*, *bidirectional*
   * **Type**: `string`
   * **Required**: *yes*
@@ -55,7 +55,7 @@ A single cell in the sequence is implemented in the same way as in <a href="#RNN
 
 * **1**: `X` - 3D tensor of type *T1* `[batch_size, seq_length, input_size]`, input data. It differs from RNNCell 1st input only by additional axis with size `seq_length`. **Required.**
 
-* **2**: `initial_hidden_state` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, input hidden state data. **Required.**
+* **2**: `H` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, input hidden state data. **Required.**
 
 * **3**: `sequence_lengths` - 1D tensor of type *T2* `[batch_size]`, specifies real sequence lengths for each batch element. **Required.**