RNNCell#

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.

Detailed description:

RNNCell represents a single RNN cell and is part of RNNSequence operation.

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

<layer ... type="RNNCell" ...>
    <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>128</dim>
            <dim>16</dim>
        </port>
        <port id="3">
            <dim>128</dim>
            <dim>128</dim>
        </port>
        <port id="4">
            <dim>128</dim>
        </port>
    </input>
    <output>
        <port id="5">
            <dim>1</dim>
            <dim>128</dim>
        </port>
    </output>
</layer>