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>