def _evaluation(self):
self.evaluation_hooks = []
self.metric_ops = {}
predictions_key = append_label(constants.PREDICT_KEY, self.name)
labels_key = append_label(constants.LABEL_KEY, self.name)
eval_tensors = { # tensors necessary for evaluation hooks (such as sequence length)
constants.LENGTH_KEY: self._sequence_lengths,
constants.SENTENCE_INDEX: self.features[constants.SENTENCE_INDEX],
labels_key: self._mask_subtokens(self.targets),
predictions_key: self.predictions,
}
overall_score = tf.identity(self.metric)
overall_key = append_label(constants.OVERALL_KEY, self.name)
self.metric_ops[overall_key] = (overall_score, overall_score)
# https://github.com/tensorflow/tensorflow/issues/20418 -- metrics don't accept variables, so we create a tensor
eval_placeholder = placeholder(dtype=tf.float32,
name='update_%s' % overall_key)
if constants.SRL_KEY in self.config.task:
if constants.MARKER_KEY in self.features:
eval_tensors[constants.MARKER_KEY] = self.features[
constants.MARKER_KEY]
else:
eval_tensors[constants.PREDICATE_INDEX_KEY] = self.features[
constants.PREDICATE_INDEX_KEY]
self.evaluation_hooks.append(
SrlEvalHook(tensors=eval_tensors,
evaluator=SrlEvaluator(target=self.extractor,
output_path=os.path.join(
self.params.job_dir,
self.name + '.dev')),
label_key=labels_key,
predict_key=predictions_key,
eval_update=assign(self.metric, eval_placeholder),
eval_placeholder=eval_placeholder,
output_confusions=self.params.verbose_eval,
output_dir=self.params.job_dir))
else:
self.evaluation_hooks.append(
SequenceEvalHook(
tensors=eval_tensors,
evaluator=TaggerEvaluator(target=self.extractor,
output_path=os.path.join(
self.params.job_dir,
self.name + '.dev')),
label_key=labels_key,
predict_key=predictions_key,
eval_update=assign(self.metric, eval_placeholder),
eval_placeholder=eval_placeholder,
output_dir=self.params.job_dir))
def _train_eval(self):
self.mask = tf.sequence_mask(self.features[constants.LENGTH_KEY],
name="padding_mask")
num_labels = self.extractor.vocab_size()
_logits = select_logits(self.logits, self.predicate_indices,
self.n_steps)
seq_mask = None if constants.BERT_LENGTH_KEY in self.features else self.features.get(
constants.SEQUENCE_MASK)
rel_loss = sequence_loss(
logits=_logits,
targets=self.targets,
sequence_lengths=self._sequence_lengths,
num_labels=num_labels,
crf=self.config.crf,
tag_transitions=self._tag_transitions,
label_smoothing=self.config.label_smoothing,
confidence_penalty=self.config.confidence_penalty,
name="bilinear_loss",
mask=seq_mask)
self.loss = rel_loss
self.metric = Variable(0,
name=append_label(constants.OVERALL_KEY,
self.name),
dtype=tf.float32,
trainable=False)
def _evaluation(self):
predictions_key = append_label(constants.PREDICT_KEY, self.name)
labels_key = append_label(constants.LABEL_KEY, self.name)
acc_key = append_label(constants.ACCURACY_METRIC_KEY, self.name)
self.metric_ops = {
acc_key:
tf.metrics.accuracy(labels=self.targets,
predictions=self.predictions,
name=acc_key)
}
tensors = {
labels_key: self.targets,
predictions_key: self.predictions,
constants.LABEL_SCORES: self.scores,
constants.LENGTH_KEY: self._sequence_lengths,
constants.SENTENCE_INDEX: self.features[constants.SENTENCE_INDEX],
}
constraint_key = self.extractor.constraint_key
if constraint_key:
tensors[constraint_key] = self.features[constraint_key]
overall_score = tf.identity(self.metric)
overall_key = append_label(constants.OVERALL_KEY, self.name)
self.metric_ops[overall_key] = (overall_score, overall_score)
# https://github.com/tensorflow/tensorflow/issues/20418 -- metrics don't accept variables, so we create a tensor
eval_placeholder = tf.placeholder(dtype=tf.float32,
name='update_%s' % overall_key)
self.evaluation_hooks = [
ClassifierEvalHook(
label_key=labels_key,
predict_key=predictions_key,
tensors=tensors,
evaluator=TokenClassifierEvaluator(target=self.extractor,
output_path=os.path.join(
self.params.job_dir,
self.name + '.dev')),
output_dir=self.params.job_dir,
eval_update=tf.assign(self.metric, eval_placeholder),
eval_placeholder=eval_placeholder,
)
]
def _evaluation(self):
# compute metrics, such as UAS, LAS, and LA
arc_correct = tf.boolean_mask(tf.to_int32(tf.equal(self.arc_predictions[:, 1:], self.arc_targets[:, 1:])),
self.mask[:, 1:])
rel_correct = tf.boolean_mask(tf.to_int32(tf.equal(self.predictions[:, 1:], self.targets[:, 1:])),
self.mask[:, 1:])
n_arc_correct = tf.cast(tf.reduce_sum(arc_correct), tf.int32)
n_rel_correct = tf.cast(tf.reduce_sum(rel_correct), tf.int32)
correct = arc_correct * rel_correct
n_correct = tf.cast(tf.reduce_sum(correct), tf.int32)
n_tokens = tf.cast(tf.reduce_sum(self.lens - 1), tf.int32) # minus 1 for sentinel
self.metric_ops = {
constants.UNLABELED_ATTACHMENT_SCORE: tf.metrics.mean(n_arc_correct / n_tokens),
constants.LABEL_SCORE: tf.metrics.mean(n_rel_correct / n_tokens),
constants.LABELED_ATTACHMENT_SCORE: tf.metrics.mean(n_correct / n_tokens),
}
overall_score = tf.identity(self.metric)
self.metric_ops[append_label(constants.OVERALL_KEY, self.name)] = (overall_score, overall_score)
overall_key = append_label(constants.OVERALL_KEY, self.name)
# https://github.com/tensorflow/tensorflow/issues/20418 -- metrics don't accept variables, so we create a tensor
eval_placeholder = tf.placeholder(dtype=tf.float32, name='update_%s' % overall_key)
self.evaluation_hooks = []
hook = ParserEvalHook(
{
constants.ARC_PROBS: self.arc_probs,
constants.REL_PROBS: self.rel_probs,
constants.LENGTH_KEY: self.lens, # plus one for the sentinel
constants.HEAD_KEY: self.features[constants.HEAD_KEY],
constants.DEPREL_KEY: self.features[constants.DEPREL_KEY],
constants.SENTENCE_INDEX: self.features[constants.SENTENCE_INDEX]
},
evaluator=DepParserEvaluator(
target=self.extractor,
output_path=os.path.join(self.params.job_dir, self.name + '.dev'),
script_path=self.params.script_path
),
eval_update=tf.assign(self.metric, eval_placeholder),
eval_placeholder=eval_placeholder,
output_dir=self.params.job_dir
)
self.evaluation_hooks.append(hook)
def __init__(self, target=None, output_path=None, script_path=None):
super().__init__(target, output_path, script_path)
self.labels = None
self.gold = None
self.indices = None
self.token_indices = None
self.target_key = constants.LABEL_KEY if not self.target.name else self.target.name
self.labels_key = constants.LABEL_KEY if not self.target.key else self.target.key
self.scores_name = append_label(constants.LABEL_SCORES, self.target_key)
def _train_eval(self):
if self.config.label_smoothing > 0:
targets = tf.one_hot(self.targets,
depth=self.extractor.vocab_size())
self.loss = tf.losses.softmax_cross_entropy(
onehot_labels=targets,
logits=self.logits,
label_smoothing=self.config.label_smoothing)
else:
self.loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.logits, labels=self.targets))
self.metric = Variable(0,
name=append_label(constants.OVERALL_KEY,
self.name),
dtype=tf.float32,
trainable=False)
def _train_eval(self):
num_labels = self.extractor.vocab_size()
seq_mask = None if constants.BERT_LENGTH_KEY in self.features else self.features.get(
constants.SEQUENCE_MASK)
self.loss = sequence_loss(
logits=self.logits,
targets=self.targets,
sequence_lengths=self._sequence_lengths,
num_labels=num_labels,
crf=self.config.crf,
tag_transitions=self._tag_transitions,
label_smoothing=self.config.label_smoothing,
confidence_penalty=self.config.confidence_penalty,
mask=seq_mask)
self.metric = Variable(0,
name=append_label(constants.OVERALL_KEY,
self.name),
dtype=tf.float32,
trainable=False)
def _train_eval(self):
self.mask = tf.sequence_mask(self.lens, name="padding_mask")
# compute combined arc and rel losses (both via softmax cross entropy)
def compute_loss(logits, targets, name):
with tf.variable_scope(name):
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets)
losses = tf.boolean_mask(losses, self.mask)
return tf.reduce_mean(losses)
self.arc_targets = tf.identity(self.features[constants.HEAD_KEY], name=constants.HEAD_KEY)
arc_loss = compute_loss(self.arc_logits, self.arc_targets, "arc_bilinear_loss")
_rel_logits = select_logits(self.rel_logits, self.arc_targets, self.n_steps)
rel_loss = compute_loss(_rel_logits, self.targets, "rel_bilinear_loss")
arc_loss = self.config.get('arc_loss_weight', 1) * arc_loss
rel_loss = self.config.get('rel_loss_weight', 1) * rel_loss
self.loss = arc_loss + rel_loss
self.metric = Variable(0, name=append_label(constants.OVERALL_KEY, self.name), dtype=tf.float32, trainable=False)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._score_name = append_label(LABEL_SCORES, self._target.name)
def _prediction(self):
super()._prediction()
self.export_outputs[append_label(constants.LABEL_SCORES,
self.name)] = self.scores
