openvino/docs/ops/sequence/LSTMCell_1.md

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# LSTMCell {#openvino_docs_ops_sequence_LSTMCell_1}
**Versioned name**: *LSTMCell-1*
**Category**: *Sequence processing*
**Short description**: *LSTMCell* operation represents a single LSTM cell. It computes the output using the formula described in the original paper [Long Short-Term Memory](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf).
**Detailed description**: *LSTMCell* computes the output *Ht* and *ot* for current time step based on the following formula:
```
Formula:
* - matrix multiplication
(.) - Hadamard product (element-wise)
[,] - concatenation
f, g, h - are activation functions.
it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf)
ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
Ct = ft (.) Ct-1 + it (.) ct
ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo)
Ht = ot (.) h(Ct)
```
**Attributes**
* *hidden_size*
* **Description**: *hidden_size* specifies hidden state size.
* **Range of values**: a positive integer
* **Type**: `int`
* **Required**: *yes*
* *activations*
* **Description**: *activations* specifies activation functions for gates, there are three gates, so three 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* for f, *tanh* for g, *tanh* for h
* **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*
**Inputs**
* **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.**
* **3**: `initial_cell_state` - 2D tensor of type *T* `[batch_size, hidden_size]`. **Required.**
* **4**: `W` - 2D tensor of type *T* `[4 * hidden_size, input_size]`, the weights for matrix multiplication, gate order: fico. **Required.**
* **5**: `R` - 2D tensor of type *T* `[4 * hidden_size, hidden_size]`, the recurrence weights for matrix multiplication, gate order: fico. **Required.**
* **6**: `B` 1D tensor of type *T* `[4 * hidden_size]`, the sum of biases (weights and recurrence weights), if not specified - assumed to be 0. **optional.**
**Outputs**
* **1**: `Ho` - 2D tensor of type *T* `[batch_size, hidden_size]`, the last output value of hidden state.
* **2**: `Co` - 2D tensor of type *T* `[batch_size, hidden_size]`, the last output value of cell state.
**Types**
* *T*: any supported floating-point type.
**Example**
```xml
<layer ... type="LSTMCell" ...>
<data hidden_size="128"/>
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="2">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="3">
<dim>512</dim>
<dim>16</dim>
</port>
<port id="4">
<dim>512</dim>
<dim>128</dim>
</port>
<port id="5">
<dim>512</dim>
</port>
</input>
<output>
<port id="6">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="7">
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>
```