def _processed_labels(self, logits, labels):
labels = base_head.check_dense_labels_match_logits_and_reshape(
labels=labels,
logits=logits,
expected_labels_dimension=self._logits_dimension)
labels = math_ops.to_float(labels)
return labels
def _processed_labels(self, logits, labels):
labels = base_head.check_dense_labels_match_logits_and_reshape(
labels=labels,
logits=logits,
expected_labels_dimension=self._logits_dimension)
labels = tf.cast(labels, dtype=tf.dtypes.float32)
return labels
def _processed_labels(self, logits, labels):
"""Converts labels to integer id space."""
labels = base_head.check_dense_labels_match_logits_and_reshape(
labels=labels, logits=logits, expected_labels_dimension=1)
if self._label_vocabulary is not None:
labels = self._class_id_table.lookup(labels)
labels = math_ops.cast(labels, dtype=dtypes.float32)
return base_head.check_label_range(labels, n_classes=2)
def _processed_labels(self, logits, labels):
"""Converts labels to integer id space."""
labels = base_head.check_dense_labels_match_logits_and_reshape(
labels=labels, logits=logits, expected_labels_dimension=1)
if self._label_vocabulary is None:
if not labels.dtype.is_integer:
raise ValueError(
'Labels dtype should be integer. Instead got {}.'.format(
labels.dtype))
label_ids = labels
else:
if labels.dtype != tf.dtypes.string:
raise ValueError('Labels dtype should be string if there is a '
'vocabulary. Instead got {}'.format(labels.dtype))
label_ids = self._class_id_table.lookup(labels)
return base_head.check_label_range(label_ids, self._n_classes)
def _processed_labels(self, logits, labels):
"""Converts labels to integer id space."""
if labels is None:
raise ValueError(base_head._LABEL_NONE_ERR_MSG) # pylint:disable=protected-access
if isinstance(labels, sparse_tensor.SparseTensor):
label_values = labels.values
if labels.dtype == dtypes.string:
label_ids_values = self._class_id_table.lookup(label_values)
label_ids = sparse_tensor.SparseTensor(
indices=labels.indices,
values=label_ids_values,
dense_shape=labels.dense_shape)
processed_labels = sparse_ops.sparse_to_indicator(
label_ids, self._n_classes)
else:
if not label_values.dtype.is_integer:
raise ValueError(
'Labels dtype should be integer. Instead got {}.'.
format(label_values.dtype))
err_msg = (
r'labels must be an integer SparseTensor with values in '
r'[0, {})'.format(self._n_classes))
label_values = base_head.check_label_range(labels.values,
self._n_classes,
message=err_msg)
if context.executing_eagerly():
processed_labels = sparse_ops.sparse_to_indicator(
labels, self._n_classes)
else:
with ops.control_dependencies([label_values]):
processed_labels = sparse_ops.sparse_to_indicator(
labels, self._n_classes)
processed_labels = math_ops.cast(processed_labels,
dtype=dtypes.int64)
else:
err_msg = (
r'labels must be an integer indicator Tensor with values in [0, 1]'
)
processed_labels = base_head.check_label_range(labels,
2,
message=err_msg)
return base_head.check_dense_labels_match_logits_and_reshape(
labels=processed_labels,
logits=logits,
expected_labels_dimension=self.logits_dimension)
