Google I/O is a wrap! Catch up on TensorFlow sessions View sessions

text.viterbi_constrained_sequence

Performs greedy constrained sequence on a batch of examples.

text.viterbi_constrained_sequence(
    scores,
    sequence_length=None,
    allowed_transitions=None,
    transition_weights=None,
    use_log_space=False,
    use_start_and_end_states=True,
    name=None
)

Constrains a set of predictions based on a set of legal transitions and/or a set of transition weights, returning the legal sequence that maximizes the product of the state scores and the transition weights according to the Viterbi algorithm. If use_log_space is True, the Viterbi calculation will be performed in log space (with sums); if it is False, the Viterbi calculation will be performed in exp space (with normalized products).

This op also takes a parameter use_start_and_end_states, which when true will add an implicit start and end state to each sequence. These implicit states allow the user to specify additional weights and permitted transitions to start and end a sequence (so, for instance, if you wanted to forbid your output from ending in a certain set of states you could do so).

Inputs to this op can take one of three forms: a single TensorFlow tensor of scores with no sequence lengths, a TensorFlow tensor of scores along with a TensorFlow tensor of sequence lengths, or a RaggedTensor. If only the scores tensor is passed, this op will assume that the sequence lengths are equal to the size of the tensor (and so use all the data provided). If a scores tensor and sequence_lengths tensor is provided, the op will only use the data in the scores tensor as specified by the sequence_lengths tensor. Finally, if a RaggedTensor is provided, the sequence_lengths will be ignored and the variable length sequences in the RaggedTensor will be used.

scores = np.array([[10.0, 12.0, 6.0, 4.0],
                   [13.0, 12.0, 11.0, 10.0]], dtype=np.float32)
sequence_length = np.array([2])
transition_weights = np.array([[ .1,  .2,  .3,  .4],
                               [ .5,  .6,  .7,  .8],
                               [ .9,  .1, .15,  .2],
                               [.25, .35, .45, .55]], dtype=np.float32)
allowed_transitions = np.array([[True,  True,  True,  True],
                                [True,  True,  True,  True],
                                [True, False,  True, False],
                                [True,  True,  True,  True]])
viterbi_constrained_sequence(
     scores=scores,
     sequence_length=sequence_length,
     allowed_transitions=allowed_transitions,
     transition_weights=transition_weights,
     use_log_space=False,
     use_start_and_end_states=False)
<tf.RaggedTensor [[1, 3]]>

scores <float32> [batch_size, num_steps, |num_states|] A tensor of scores, where scores[b, t, s] is the predicted score for transitioning to state s at step t for batch b. The |num_states| dimension must correspond to the num_states attribute for this op. This input may be ragged; if it is ragged, the ragged tensor should have the same structure [b, t, s] and only axis 1 should be ragged.
sequence_length <{int32, int64}>[batch_size] A rank-1 tensor representing the length of the output sequence. If None, and the 'scores' input is not ragged, sequence lengths will be assumed to be the length of the score tensor.
allowed_transitions if use_start_and_end_states is TRUE: <bool>[num_states+1, num_states+1] if use_start_and_end_states is FALSE: <bool>[num_states, num_states] A rank-2 tensor representing allowed transitions.

  • allowed_transitions[i][j] is true if the transition from state i to state j is allowed for i and j in 0...(num_states).
  • allowed_transitions[num_states][num_states] is ignored. If use_start_and_end_states is TRUE:
    • allowed_transitions[num_states][j] is true if the sequence is allowed to start from state j.
    • allowed_transitions[i][num_states] is true if the sequence is allowed to end on state i. Default - An empty tensor. This allows all sequence states to transition to all other sequence states.
transition_weights if use_start_and_end_states is TRUE: <float32>[num_states+1, num_states+1] if use_start_and_end_states is FALSE: <float32>[num_states, num_states] A rank-2 tensor representing transition weights.
  • transition_weights[i][j] is the coefficient that a candidate transition score will be multiplied by if that transition is from state i to state j.
  • transition_weights[num_states][num_states] is ignored. If use_start_and_end_states is TRUE:
    • transition_weights[num_states][j] is the coefficient that will be used if the transition starts with state j.
    • transition_weights[i][num_states] is the coefficient that will be used if the final state in the sequence is state i. Default - An empty tensor. This assigns a wieght of 1.0 all transitions
    • use_log_space Whether to use log space for the calculation. If false, calculations will be done in exp-space.
    use_start_and_end_states If True, sequences will have an implicit start and end state added.
    name The name scope within which this op should be constructed.

    An [batch_size, (num_steps)] ragged tensor containing the appropriate sequence of transitions. If a sequence is impossible, the value of the RaggedTensor for that and all following transitions in that sequence shall be '-1'.