本文整理汇总了Python中tensorflow.python.ops.parsing_ops.parse_single_sequence_example方法的典型用法代码示例。如果您正苦于以下问题：Python parsing_ops.parse_single_sequence_example方法的具体用法？Python parsing_ops.parse_single_sequence_example怎么用？Python parsing_ops.parse_single_sequence_example使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.parsing_ops的用法示例。
在下文中一共展示了parsing_ops.parse_single_sequence_example方法的2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: decode
# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import parse_single_sequence_example [as 别名]
def decode(self, serialized_example, items=None):
"""Decodes the given serialized TF-SequenceExample.
Args:
serialized_example: a serialized TF-SequenceExample tensor.
items: the list of items to decode. These must be a subset of the item
keys in self._items_to_handlers. If `items` is left as None, then all
of the items in self._items_to_handlers are decoded.
Returns:
the decoded items, a list of tensor.
"""
context, feature_list = parsing_ops.parse_single_sequence_example(
serialized_example, self._keys_to_context_features,
self._keys_to_sequence_features)
# Reshape non-sparse elements just once:
for k in self._keys_to_context_features:
v = self._keys_to_context_features[k]
if isinstance(v, parsing_ops.FixedLenFeature):
context[k] = array_ops.reshape(context[k], v.shape)
if not items:
items = self._items_to_handlers.keys()
outputs = []
for item in items:
handler = self._items_to_handlers[item]
keys_to_tensors = {
key: context[key] if key in context else feature_list[key]
for key in handler.keys
}
outputs.append(handler.tensors_to_item(keys_to_tensors))
return outputs 示例2: parse_feature_columns_from_sequence_examples
# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import parse_single_sequence_example [as 别名]
def parse_feature_columns_from_sequence_examples(
serialized,
context_feature_columns,
sequence_feature_columns,
name=None,
example_name=None):
"""Parses tf.SequenceExamples to extract tensors for given `FeatureColumn`s.
Args:
serialized: A scalar (0-D Tensor) of type string, a single serialized
`SequenceExample` proto.
context_feature_columns: An iterable containing the feature columns for
context features. All items should be instances of classes derived from
`_FeatureColumn`. Can be `None`.
sequence_feature_columns: An iterable containing the feature columns for
sequence features. All items should be instances of classes derived from
`_FeatureColumn`. Can be `None`.
name: A name for this operation (optional).
example_name: A scalar (0-D Tensor) of type string (optional), the names of
the serialized proto.
Returns:
A tuple consisting of:
context_features: a dict mapping `FeatureColumns` from
`context_feature_columns` to their parsed `Tensors`/`SparseTensor`s.
sequence_features: a dict mapping `FeatureColumns` from
`sequence_feature_columns` to their parsed `Tensors`/`SparseTensor`s.
"""
# Sequence example parsing requires a single (scalar) example.
try:
serialized = array_ops.reshape(serialized, [])
except ValueError as e:
raise ValueError(
'serialized must contain as single sequence example. Batching must be '
'done after parsing for sequence examples. Error: {}'.format(e))
if context_feature_columns is None:
context_feature_columns = []
if sequence_feature_columns is None:
sequence_feature_columns = []
check_feature_columns(context_feature_columns)
context_feature_spec = fc.create_feature_spec_for_parsing(
context_feature_columns)
check_feature_columns(sequence_feature_columns)
sequence_feature_spec = fc._create_sequence_feature_spec_for_parsing( # pylint: disable=protected-access
sequence_feature_columns, allow_missing_by_default=False)
return parsing_ops.parse_single_sequence_example(serialized,
context_feature_spec,
sequence_feature_spec,
example_name,
name)