CrfDecodeForwardRnnCell Objects

class CrfDecodeForwardRnnCell(tf.keras.layers.AbstractRNNCell)

Computes the forward decoding in a linear-chain CRF.

init

def __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
def output_size() -> int

Returns count of tags.

build

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

Creates the variables of the layer.

call

def 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

def 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

def 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

def 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.

crf_unary_score

def crf_unary_score(tag_indices: TensorLike, sequence_lengths: TensorLike, inputs: TensorLike) -> tf.Tensor

Computes the unary scores of tag sequences.

Arguments:

• tag_indices - A [batch_size, max_seq_len] matrix of tag indices.
• sequence_lengths - A [batch_size] vector of true sequence lengths.
• inputs - A [batch_size, max_seq_len, num_tags] tensor of unary potentials.

Returns:

• unary_scores - A [batch_size] vector of unary scores.

crf_binary_score

def crf_binary_score(tag_indices: TensorLike, sequence_lengths: TensorLike, transition_params: TensorLike) -> tf.Tensor

Computes the binary scores of tag sequences.

Arguments:

• tag_indices - A [batch_size, max_seq_len] matrix of tag indices.
• sequence_lengths - A [batch_size] vector of true sequence lengths.
• transition_params - A [num_tags, num_tags] matrix of binary potentials.

Returns:

• binary_scores - A [batch_size] vector of binary scores.

crf_sequence_score

def crf_sequence_score(inputs: TensorLike, tag_indices: TensorLike, sequence_lengths: TensorLike, transition_params: TensorLike) -> tf.Tensor

Computes the unnormalized score for a tag sequence.

Arguments:

• inputs - A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer.
• tag_indices - A [batch_size, max_seq_len] matrix of tag indices for which we compute the unnormalized score.
• sequence_lengths - A [batch_size] vector of true sequence lengths.
• transition_params - A [num_tags, num_tags] transition matrix.

Returns:

• sequence_scores - A [batch_size] vector of unnormalized sequence scores.

crf_forward

def crf_forward(inputs: TensorLike, state: TensorLike, transition_params: TensorLike, sequence_lengths: TensorLike) -> tf.Tensor

Computes the alpha values in a linear-chain CRF.

See http://www.cs.columbia.edu/~mcollins/fb.pdf for reference.

Arguments:

• inputs - A [batch_size, num_tags] matrix of unary potentials.
• state - A [batch_size, num_tags] matrix containing the previous alpha values.
• 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.
• sequence_lengths - A [batch_size] vector of true sequence lengths.

Returns:

• new_alphas - A [batch_size, num_tags] matrix containing the new alpha values.

crf_log_norm

def crf_log_norm(inputs: TensorLike, sequence_lengths: TensorLike, transition_params: TensorLike) -> tf.Tensor

Computes the normalization for a CRF.

Arguments:

• inputs - A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer.
• sequence_lengths - A [batch_size] vector of true sequence lengths.
• transition_params - A [num_tags, num_tags] transition matrix.

Returns:

• log_norm - A [batch_size] vector of normalizers for a CRF.

crf_log_likelihood

def crf_log_likelihood(inputs: TensorLike, tag_indices: TensorLike, sequence_lengths: TensorLike, transition_params: Optional[TensorLike] = None) -> Tuple[tf.Tensor, tf.Tensor]

Computes the log-likelihood of tag sequences in a CRF.

Arguments:

• inputs - A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer.
• tag_indices - A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likelihood.
• sequence_lengths - A [batch_size] vector of true sequence lengths.
• transition_params - A [num_tags, num_tags] transition matrix, if available.

Returns:

• log_likelihood - A [batch_size] Tensor containing the log-likelihood of each example, given the sequence of tag indices.
• transition_params - A [num_tags, num_tags] transition matrix. This is either provided by the caller or created in this function.