def build_eval_metrics(self, logits, labels):
""" Building evaluation metrics while evaluating
Args:
logits (`Tensor`): list of tensors shape of [None, num_labels]
labels (`Tensor`): shape of [None]
Returns:
ret_dict (`dict`): A dict of each layer accuracy tf.metrics op
"""
if self.config.train_probes:
return teacher_probes_eval_metrics(logits, labels,
self.config.num_labels)
else:
return classification_eval_metrics(logits[0], labels,
self.config.num_labels)
def build_eval_metrics(self, logits, labels):
""" Building evaluation metrics while evaluating
Args:
logits (`Tensor`): shape of [None, num_labels]
labels (`Tensor`): shape of [None]
Returns:
ret_dict (`dict`): A dict with (`py_accuracy`, `py_micro_f1`, `py_macro_f1`) tf.metrics op
"""
if hasattr(self.config, "multi_label") and self.config.multi_label:
return multi_label_eval_metrics(logits, labels, self.config.num_labels)
elif self.config.num_labels == 1:
return regression_eval_metrics(logits, labels)
else:
return classification_eval_metrics(logits, labels, self.config.num_labels)
def build_eval_metrics(self, logits, labels):
return classification_eval_metrics(logits, labels, self.num_labels)
def build_eval_metrics(self, logits, labels):
if _APP_FLAGS.task_name == "CoLA":
return matthew_corr_metrics(logits, labels)
else:
return classification_eval_metrics(logits, labels, self.num_labels)
