openvino/docs/ops/sequence/GRUCell_3.md

GRUCell

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Versioned name: GRUCell-3

Category: Sequence processing

Short description: GRUCell represents a single GRU Cell that computes the output using the formula described in the paper <https://arxiv.org/abs/1406.1078>__.

Detailed description: GRUCell computes the output Ht for the current time step based on the followint formula:

.. code-block::

Formula: * - matrix multiplication (.) - Hadamard product(element-wise) [,] - concatenation f, g - are activation functions. zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) ht = g(Xt*(Wh^T) + (rt (.) Ht-1)(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 ht = g(Xt(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 Ht = (1 - zt) (.) ht + zt (.) Ht-1

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: sigmoid for f, tanh for g
  • 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

• 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 - 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: W - 2D tensor of type T [3 * hidden_size, input_size], the weights for matrix multiplication, gate order: zrh. Required.
• 4: R - 2D tensor of type T [3 * hidden_size, hidden_size], the recurrence weights for matrix multiplication, gate order: zrh. Required.
• 5: B - 1D tensor of type T. If linear_before_reset is set to 1, then the shape is [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 [3 * hidden_size], the sum of biases (weights and recurrence weights). Optional.

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="GRUCell" ...> 1 16 1 128 384 16 384 128 768 1 128

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