Versioned name: LSTMSequence-1

Category: Sequence processing

Short description: LSTMSequence operation represents a series of LSTM cells. Each cell is implemented as LSTMCell operation.

Detailed description

A single cell in the sequence is implemented in the same way as in LSTMCell operation. LSTMSequence represents a sequence of LSTM 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 LSTM operator defined LSTMCell.

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: activations specifies activation functions for gates, there are three gates, so three activation functions should be specified as a value for this attributes
  • Range of values: any combination of relu, sigmoid, tanh
  • Type: a list of strings
  • Default value: sigmoid,tanh,tanh
  • Required: no
• activations_alpha, activations_beta
  • Description: activations_alpha, activations_beta attributes of functions; applicability and meaning of these attributes depends on choosen 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 ([batch_size, seq_length, input_size]) input data. It differs from LSTMCell 1st input only by additional axis with size seq_length. Floating point type. Required.
• 2: initial_hidden_state - 3D ([batch_size, num_directions, hidden_size]) input hidden state data. Floating point type. Required.
• 3: initial_cell_state - 3D ([batch_size, num_directions, hidden_size]) input cell state data. Floating point type. Required.
• 4: sequence_lengths - 1D ([batch_size]) specifies real sequence lengths for each batch element. Integer type. Required.
• 5: W - 3D tensor with weights for matrix multiplication operation with input portion of data, shape is [num_directions, 4 * hidden_size, input_size], output gate order: fico. Floating point type. Required.
• 6: R - 3D tensor with weights for matrix multiplication operation with hidden state, shape is [num_directions, 4 * hidden_size, hidden_size], output gate order: fico. Floating point type. Required.
• 7: B - 2D tensor with biases, shape is [num_directions, 4 * hidden_size]. Floating point type. Required.

Outputs

• 1: Y – 3D output, shape [batch_size, num_directions, seq_len, hidden_size]
• 2: Ho - 3D ([batch_size, num_directions, hidden_size]) output hidden state.
• 3: Co - 3D ([batch_size, num_directions, hidden_size]) output cell state.