## GRUSequence <a name="GRUSequence"></a> {#openvino_docs_ops_sequence_GRUSequence_5}

**Versioned name**: *GRUSequence-5*

**Category**: *Sequence processing*

**Short description**: *GRUSequence* operation represents a series of GRU cells. Each cell is implemented as <a href="#GRUCell">GRUCell</a> operation.

**Detailed description**

A single cell in the sequence is implemented in the same way as in <a href="#GRUCell">GRUCell</a> operation. *GRUSequence* represents a sequence of GRU cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX GRU operator defined <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#gru">GRUCell</a>.


**Attributes**

* *hidden_size*

  * **Description**: *hidden_size* specifies hidden state size.
  * **Range of values**: a positive integer
  * **Type**: `int`
  * **Default value**: None
  * **Required**: *yes*

* *activations*

  * **Description**: *activations* specifies activation functions for gates, there are two gates, so two activation functions should be specified as a value for this attributes
  * **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
  * **Type**: a list of strings
  * **Default value**: *sigmoid,tanh*
  * **Required**: *no*

* *activations_alpha, activations_beta*

  * **Description**: *activations_alpha, activations_beta* attributes of functions; applicability and meaning of these attributes depends on chosen activation functions
  * **Range of values**: a list of floating-point numbers
  * **Type**: `float[]`
  * **Default value**: None
  * **Required**: *no*

* *clip*

  * **Description**: *clip* specifies bound values *[-C, C]* for tensor clipping. Clipping is performed before activations.
  * **Range of values**: a positive floating-point number
  * **Type**: `float`
  * **Default value**: *infinity* that means that the clipping is not applied
  * **Required**: *no*

* *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.
  * **Range of values**: *forward*, *reverse*, *bidirectional*
  * **Type**: `string`
  * **Default value**: None
  * **Required**: *Yes*

* *linear_before_reset*

  * **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification of *GRUCell* described in the formula in the [ONNX documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU).
  * **Range of values**: True or False
  * **Type**: `boolean`
  * **Default value**: False
  * **Required**: *no*

**Inputs**

* **1**: `X` - 3D tensor of type *T1* `[batch_size, seq_length, input_size]`, input data. It differs from GRUCell 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.**

* **3**: `sequence_lengths` - 1D tensor of type *T2* `[batch_size]`, specifies real sequence lengths for each batch element. **Required.**

* **4**: `W` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, input_size]`, the weights for matrix multiplication, gate order: zrh. **Required.**

* **5**: `R` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, hidden_size]`, the recurrence weights for matrix multiplication, gate order: zrh. **Required.**

* **6**: `B` - 2D tensor of type *T*. If *linear_before_reset* is set to 1, then the shape is `[num_directions, 4 * hidden_size]` - the sum of biases for z and r gates (weights and recurrence weights), the biases for h gate are placed separately. Otherwise the shape is `[num_directions, 3 * hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**

**Outputs**

* **1**: `Y` - 4D tensor of type *T1* `[batch_size, num_directions, seq_len, hidden_size]`, concatenation of all the intermediate output values of the hidden.

* **2**: `Ho` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, the last output value of hidden state.

**Types**

* *T1*: any supported floating point type.
* *T2*: any supported integer type.

**Example**
```xml
<layer ... type="GRUSequence" ...>
    <data hidden_size="128"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>4</dim>
            <dim>16</dim>
        </port>
        <port id="1">
            <dim>1</dim>
            <dim>1</dim>
            <dim>128</dim>
        </port>
        <port id="2">
            <dim>1</dim>
        </port>
         <port id="3">
            <dim>1</dim>
            <dim>384</dim>
            <dim>16</dim>
        </port>
         <port id="4">
            <dim>1</dim>
            <dim>384</dim>
            <dim>128</dim>
        </port>
         <port id="5">
            <dim>1</dim>
            <dim>384</dim>
        </port>
    </input>
    <output>
        <port id="6">
            <dim>1</dim>
            <dim>1</dim>
            <dim>4</dim>
            <dim>128</dim>
        </port>
        <port id="7">
            <dim>1</dim>
            <dim>1</dim>
            <dim>128</dim>
        </port>
    </output>
</layer>
```