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This is documentation for Rasa & Rasa Pro Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).

Version: 2.x

rasa.utils.tensorflow.crf

CrfDecodeForwardRnnCell Objects

class CrfDecodeForwardRnnCell(tf.keras.layers.AbstractRNNCell)

Computes the forward decoding in a linear-chain CRF.

__init__

| @typechecked
| __init__(transition_params: TensorLike, **kwargs: Any) -> None

Initialize the CrfDecodeForwardRnnCell.

Arguments:

  • transition_params - A [num_tags, num_tags] matrix of binary potentials. This matrix is expanded into a [1, num_tags, num_tags] in preparation for the broadcast summation occurring within the cell.

output_size

| @property
| output_size() -> int

Returns count of tags.

build

| build(input_shape: Union[TensorShape, List[TensorShape]]) -> None

Creates the variables of the layer.

call

| call(inputs: TensorLike, state: TensorLike) -> Tuple[tf.Tensor, tf.Tensor]

Build the CrfDecodeForwardRnnCell.

Arguments:

  • inputs - A [batch_size, num_tags] matrix of unary potentials.
  • state - A [batch_size, num_tags] matrix containing the previous step's score values.

Returns:

  • output - A [batch_size, num_tags * 2] matrix of backpointers and scores.
  • new_state - A [batch_size, num_tags] matrix of new score values.

crf_decode_forward

crf_decode_forward(inputs: TensorLike, state: TensorLike, transition_params: TensorLike, sequence_lengths: TensorLike) -> Tuple[tf.Tensor, tf.Tensor]

Computes forward decoding in a linear-chain CRF.

Arguments:

  • inputs - A [batch_size, num_tags] matrix of unary potentials.
  • state - A [batch_size, num_tags] matrix containing the previous step's score values.
  • transition_params - A [num_tags, num_tags] matrix of binary potentials.
  • sequence_lengths - A [batch_size] vector of true sequence lengths.

Returns:

  • output - A [batch_size, num_tags * 2] matrix of backpointers and scores.
  • new_state - A [batch_size, num_tags] matrix of new score values.

crf_decode_backward

crf_decode_backward(backpointers: TensorLike, scores: TensorLike, state: TensorLike) -> Tuple[tf.Tensor, tf.Tensor]

Computes backward decoding in a linear-chain CRF.

Arguments:

  • backpointers - A [batch_size, num_tags] matrix of backpointer of next step (in time order).
  • scores - A [batch_size, num_tags] matrix of scores of next step (in time order).
  • state - A [batch_size, 1] matrix of tag index of next step.

Returns:

  • new_tags - A [batch_size, num_tags] tensor containing the new tag indices.
  • new_scores - A [batch_size, num_tags] tensor containing the new score values.

crf_decode

crf_decode(potentials: TensorLike, transition_params: TensorLike, sequence_length: TensorLike) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]

Decode the highest scoring sequence of tags.

Arguments:

  • potentials - A [batch_size, max_seq_len, num_tags] tensor of unary potentials.
  • transition_params - A [num_tags, num_tags] matrix of binary potentials.
  • sequence_length - A [batch_size] vector of true sequence lengths.

Returns:

  • decode_tags - A [batch_size, max_seq_len] matrix, with dtype tf.int32. Contains the highest scoring tag indices.
  • decode_scores - A [batch_size, max_seq_len] matrix, containing the score of decode_tags.
  • best_score - A [batch_size] vector, containing the best score of decode_tags.