def AddEvaluation(self,
task_context,
batch_size,
evaluation_max_steps=300,
corpus_name=None,
value=""):
with tf.name_scope('evaluation'):
n = self.evaluation
n.update(
self._AddBeamReader(task_context,
batch_size,
corpus_name,
until_all_final=True,
always_start_new_sentences=True,
value=value))
self._BuildNetwork(list(n['features']),
return_average=self._use_averaging)
n.update(
self._BuildSequence(batch_size,
evaluation_max_steps,
n['features'],
n['state'],
use_average=self._use_averaging))
n['eval_metrics'], n['documents'] = (
gen_parser_ops.beam_eval_output(n['state']))
return n
def AddEvaluation(self,
task_context,
batch_size,
evaluation_max_steps=300,
corpus_name=None):
with tf.name_scope('evaluation'):
n = self.evaluation
n.update(self._AddBeamReader(task_context,
batch_size,
corpus_name,
until_all_final=True,
always_start_new_sentences=True))
self._BuildNetwork(
list(n['features']),
return_average=self._use_averaging)
n.update(self._BuildSequence(batch_size, evaluation_max_steps, n[
'features'], n['state'], use_average=self._use_averaging))
n['eval_metrics'], n['documents'] = (
gen_parser_ops.beam_eval_output(n['state']))
return n