# RNNSequence¶

Versioned name : RNNSequence-5

Category : Sequence processing

Short description : RNNSequence operation represents a series of RNN cells. Each cell is implemented as RNNCell operation.

Detailed description

A single cell in the sequence is implemented in the same way as in RNNCell operation. RNNSequence represents a sequence of RNN cells. The sequence can be connected differently depending on direction attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX RNN operator defined RNNCell.

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 : tanh

• Required : no

• activations_alpha, activations_beta

• Description : activations_alpha, activations_beta attributes of functions; applicability and meaning of these attributes depends on chosen activation functions

• Range of values : a list of floating-point numbers

• Type : float[]

• Default value : None

• Required : no

• clip

• Description : clip specifies bound values [-C, C] for tensor clipping. Clipping is performed 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

• direction

• Description : Specify if the RNN is forward, reverse, or bidirectional. If it is one of forward or reverse then num_directions = 1, if it is bidirectional, then num_directions = 2. This num_directions value specifies input/output shape requirements.

• Range of values : forward, reverse, bidirectional

• Type : string

• Default value : None

• Required : Yes

Inputs

• 1 : X - 3D tensor of type T1 [batch_size, seq_length, input_size], input data. It differs from RNNCell 1st input only by additional axis with size seq_length. Required.

• 2 : initial_hidden_state - 3D tensor of type T1 [batch_size, num_directions, hidden_size], input hidden state data. Required.

• 3 : sequence_lengths - 1D tensor of type T2 [batch_size], specifies real sequence lengths for each batch element. Required.

• 4 : W - 3D tensor of type T1 [num_directions, hidden_size, input_size], the weights for matrix multiplication. Required.

• 5 : R - 3D tensor of type T1 [num_directions, hidden_size, hidden_size], the recurrence weights for matrix multiplication. Required.

• 6 : B - 2D tensor of type T1 [num_directions, hidden_size], the sum of biases (weights and recurrence weights). Required.

Outputs

• 1 : Y - 4D tensor of type T1 [batch_size, num_directions, seq_len, hidden_size], concatenation of all the intermediate output values of the hidden.

• 2 : Ho - 3D tensor of type T1 [batch_size, num_directions, hidden_size], the last output value of hidden state.

Types

• T1 : any supported floating point type.

• T2 : any supported integer type.

Example

<layer ... type="RNNSequence" ...>
<data hidden_size="128"/>
<input>
<port id="0">
<dim>1</dim>
<dim>4</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
</port>
<port id="2">
<dim>1</dim>
</port>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>16</dim>
</port>
<port id="4">
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="5">
<dim>1</dim>
<dim>128</dim>
</port>
</input>
<output>
<port id="6">
<dim>1</dim>
<dim>1</dim>
<dim>4</dim>
<dim>128</dim>
</port>
<port id="7">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>