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openvino/docs/ops/sequence/RNNCell_1.md
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Updated from 2020.3 to 2020.4

Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
2020-07-20 17:36:08 +03:00

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RNNCell

Versioned name: RNNCell-1

Category: Sequence processing

Short description: RNNCell represents a single RNN cell that computes the output using the formula described in the article.

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: activation functions for gates
    • 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 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 ([batch_size, input_size]) input data. Required.

  • 2: initial_hidden_state - 2D ([batch_size, hidden_size]) input hidden state data. Required.

Outputs

  • 1: Ho - 2D ([batch_size, hidden_size]) output hidden state.