本文整理汇总了Python中tensorflow.python.ops.sparse_ops.sparse_to_indicator函数的典型用法代码示例。如果您正苦于以下问题：Python sparse_to_indicator函数的具体用法？Python sparse_to_indicator怎么用？Python sparse_to_indicator使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了sparse_to_indicator函数的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _process_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)示例2: _process_labels
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)示例3: testInt64
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)示例4: testInt64
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)示例5: testHigherRank
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)示例6: testHigherRank
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)示例7: _process_labels
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,示例8: _process_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)