openvino/docs/ops/sequence/RNNCell_3.md

RNNCell

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Versioned name: RNNCell-3

Category: Sequence processing

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 :doc:RNNSequence <openvino_docs_ops_sequence_RNNSequence_5> operation.

.. code-block:: cpp

Formula: * - matrix multiplication ^T - matrix transpose f - activation function Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)

Attributes

• hidden_size

  • Description: hidden_size specifies hidden state size.
  • Range of values: a positive integer
  • Type: int
  • Required: yes

• activations

  • Description: activation functions for gates
  • Range of values: any combination of relu, sigmoid, tanh
  • Type: a list of strings
  • Default value: tanh
  • Required: no

• activations_alpha, activations_beta

  • Description: activations_alpha, activations_beta functions attributes
  • Range of values: a list of floating-point numbers
  • Type: float[]
  • Default value: None
  • Required: no

• clip

  • Description: clip specifies value for tensor clipping to be in [-C, C] 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: H - 2D tensor of type T [batch_size, hidden_size], initial hidden state. Required.

• 3: W - 2D tensor of type T [hidden_size, input_size], the weights for matrix multiplication. Required.

• 4: R - 2D tensor of type T [hidden_size, hidden_size], the recurrence weights for matrix multiplication. Required.

• 5: B 1D tensor of type T [hidden_size], the sum of biases (weights and recurrence weights). Required.

Outputs

• 1: Ho - 2D tensor of type T [batch_size, hidden_size], the last output value of hidden state.

Types

• T: any supported floating-point type.

Example

.. code-block:: cpp

<layer ... type="RNNCell" ...> 1 16 1 128 128 16 128 128 128 1 128

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