openvino/docs/ops/sequence/LSTMCell_1.md

LSTMCell

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.. meta:: :description: Learn about LSTMCell-1 - a sequence processing operation, which can be performed on five required and one optional input tensor.

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 <https://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:

.. code-block:: sh

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

.. code-block:: xml :force:

<layer ... type="LSTMCell" ...> 1 16 1 128 1 128 512 16 512 128 512 1 128 1 128

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