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)
else:
label_ids = labels
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(label_ids, self._n_classes))
msg = ('labels shape must be [batch_size, {}]. '
'Given: ').format(self._n_classes)
labels_shape = array_ops.shape(labels)
check_rank_op = control_flow_ops.Assert(
math_ops.equal(array_ops.rank(labels), 2),
data=[msg, labels_shape])
check_label_dim = control_flow_ops.Assert(
math_ops.equal(labels_shape[-1], self._n_classes),
data=[msg, labels_shape])
with ops.control_dependencies([check_rank_op, check_label_dim]):
return array_ops.identity(labels)
def _process_labels(self, 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)
else:
label_ids = labels
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(label_ids, self._n_classes))
msg = ('labels shape must be [batch_size, {}]. '
'Given: ').format(self._n_classes)
labels_shape = array_ops.shape(labels)
check_rank_op = control_flow_ops.Assert(
math_ops.equal(array_ops.rank(labels), 2),
data=[msg, labels_shape])
check_label_dim = control_flow_ops.Assert(
math_ops.equal(labels_shape[-1], self._n_classes),
data=[msg, labels_shape])
with ops.control_dependencies([check_rank_op, check_label_dim]):
return array_ops.identity(labels)
def testInt64(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_5x6(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50).eval()
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testInt64(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50)
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_2x3x4(types.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200).eval()
expected_output = np.zeros((2, 3, 200), dtype=np.bool)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12), (1, 0, 103), (1, 1, 111), (1, 1, 113), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testInt64(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50)
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32),
(3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x3x4(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200)
expected_output = np.zeros((2, 3, 200), dtype=np.bool)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12), (1, 0, 103),
(1, 1, 149), (1, 1, 150), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testInt64(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_5x6(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50).eval()
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32),
(3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_2x3x4(types.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200).eval()
expected_output = np.zeros((2, 3, 200), dtype=np.bool)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12), (1, 0, 103),
(1, 1, 111), (1, 1, 113), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x3x4(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200)
expected_output = np.zeros((2, 3, 200), dtype=np.bool)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12), (1, 0, 103),
(1, 1, 149), (1, 1, 150), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
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 _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.to_int64(processed_labels)
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)
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 isinstance(labels, sparse_tensor.SparseTensor):
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(labels, self._n_classes))
msg = ('labels shape must be [batch_size, {}]. '
'Given: ').format(self._n_classes)
labels_shape = array_ops.shape(labels)
check_rank_op = control_flow_ops.Assert(
math_ops.equal(array_ops.rank(labels), 2),
data=[msg, labels_shape])
check_label_dim = control_flow_ops.Assert(
math_ops.equal(labels_shape[-1], self._n_classes),
data=[msg, labels_shape])
with ops.control_dependencies([check_rank_op, check_label_dim]):
return array_ops.identity(labels)
def _process_labels(self, labels):
if isinstance(labels, sparse_tensor.SparseTensor):
return math_ops.to_int64(
sparse_ops.sparse_to_indicator(labels, self._n_classes))
msg = ('labels shape must be [batch_size, {}]. '
'Given: ').format(self._n_classes)
labels_shape = array_ops.shape(labels)
check_rank_op = control_flow_ops.Assert(math_ops.equal(
array_ops.rank(labels), 2),
data=[msg, labels_shape])
check_label_dim = control_flow_ops.Assert(math_ops.equal(
labels_shape[-1], self._n_classes),
data=[msg, labels_shape])
with ops.control_dependencies([check_rank_op, check_label_dim]):
return array_ops.identity(labels)