def fast_baum_welch_by_sprint_automata(am_scores,
float_idx,
tags,
sprint_opts,
tdp_scale=1.0):
"""
:param tf.Tensor am_scores: (time, batch, dim), in -log space
:param tf.Tensor float_idx: (time, batch) -> 0 or 1 (index mask, via seq lens)
:param tf.Tensor tags: (batch,) -> seq name (str)
:param float tdp_scale: weights are multiplied by this
:param dict[str] sprint_opts:
:return: (fwdbwd, obs_scores), fwdbwd is (time, batch, dim), obs_scores is (time, batch), in -log space
:rtype: (tf.Tensor, tf.Tensor)
"""
from TFSprint import get_sprint_automata_for_batch_op
edges, weights, start_end_states = get_sprint_automata_for_batch_op(
sprint_opts=sprint_opts, tags=tags)
if tdp_scale != 1:
if tdp_scale == 0:
weights = tf.zeros_like(weights)
else:
weights *= tdp_scale
return fast_baum_welch(am_scores=am_scores,
float_idx=float_idx,
edges=edges,
weights=weights,
start_end_states=start_end_states)
def fast_baum_welch_by_sprint_automata(am_scores, float_idx, tags,
sprint_opts):
"""
:param tf.Tensor am_scores: (time, batch, dim), in -log space
:param tf.Tensor float_idx: (time, batch) -> 0 or 1 (index mask, via seq lens)
:param tf.Tensor tags: (batch,) -> seq name (str)
:param dict[str] sprint_opts:
:return: (fwdbwd, obs_scores), fwdbwd is (time, batch, dim), obs_scores is (time, batch), in -log space
:rtype: (tf.Tensor, tf.Tensor)
"""
from TFSprint import get_sprint_automata_for_batch_op
edges, weights, start_end_states = get_sprint_automata_for_batch_op(
sprint_opts=sprint_opts, tags=tags)
return fast_baum_welch(am_scores=am_scores,
float_idx=float_idx,
edges=edges,
weights=weights,
start_end_states=start_end_states)