def _logistic_loss(labels, logits):
labels = head_lib._assert_range( # pylint:disable=protected-access
labels,
n_classes=2,
message='Labels must be in range [0, 1]')
return nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=logits)
def _process_labels(self, labels):
if labels is None:
raise ValueError(
'You must provide a labels Tensor. Given: None. '
'Suggested troubleshooting steps: Check that your data contain '
'your label feature. Check that your input_fn properly parses and '
'returns labels.')
if isinstance(labels, sparse_tensor.SparseTensor):
if labels.dtype == dtypes.string:
label_ids_values = lookup_ops.index_table_from_tensor(
vocabulary_list=tuple(self._label_vocabulary),
name='class_id_lookup').lookup(labels.values)
label_ids = sparse_tensor.SparseTensor(
indices=labels.indices,
values=label_ids_values,
dense_shape=labels.dense_shape)
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(label_ids, self._n_classes))
else:
err_msg = (
r'labels must be an integer SparseTensor with values in '
r'[0, {})'.format(self._n_classes))
assert_int = check_ops.assert_integer(labels.values,
message=err_msg)
assert_less = check_ops.assert_less(labels.values,
ops.convert_to_tensor(
self._n_classes,
dtype=labels.dtype),
message=err_msg)
assert_greater = check_ops.assert_non_negative(labels.values,
message=err_msg)
with ops.control_dependencies(
[assert_int, assert_less, assert_greater]):
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(labels,
self._n_classes))
err_msg = (
r'labels must be an integer indicator Tensor with values in [0, 1]'
)
return head_lib._assert_range(labels, 2, message=err_msg) # pylint:disable=protected-access,
def _process_labels(self, labels):
if labels is None:
raise ValueError(
'You must provide a labels Tensor. Given: None. '
'Suggested troubleshooting steps: Check that your data contain '
'your label feature. Check that your input_fn properly parses and '
'returns labels.')
if isinstance(labels, sparse_tensor.SparseTensor):
if labels.dtype == dtypes.string:
label_ids_values = lookup_ops.index_table_from_tensor(
vocabulary_list=tuple(self._label_vocabulary),
name='class_id_lookup').lookup(labels.values)
label_ids = sparse_tensor.SparseTensor(
indices=labels.indices,
values=label_ids_values,
dense_shape=labels.dense_shape)
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(label_ids, self._n_classes))
else:
err_msg = (
r'labels must be an integer SparseTensor with values in '
r'[0, {})'.format(self._n_classes))
assert_int = check_ops.assert_integer(
labels.values, message=err_msg)
assert_less = check_ops.assert_less(
labels.values,
ops.convert_to_tensor(self._n_classes, dtype=labels.dtype),
message=err_msg)
assert_greater = check_ops.assert_non_negative(
labels.values, message=err_msg)
with ops.control_dependencies(
[assert_int, assert_less, assert_greater]):
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(labels, self._n_classes))
err_msg = (
r'labels must be an integer indicator Tensor with values in [0, 1]')
return head_lib._assert_range(labels, 2, message=err_msg) # pylint:disable=protected-access,
def _logistic_loss(labels, logits):
labels = head_lib._assert_range( # pylint:disable=protected-access
labels, n_classes=2, message='Labels must be in range [0, 1]')
return nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=logits)
