name: Optional op name.
Returns:
The tensor.
"""
with ops.name_scope(name, 'lt_identity', [labeled_tensor]) as scope:
labeled_tensor = convert_to_labeled_tensor(labeled_tensor)
return LabeledTensor(
array_ops.identity(labeled_tensor.tensor, name=scope),
labeled_tensor.axes)
# We don't call this slice because that shadows a built-in. Instead, we alias
# this to lt.slice in __init__.py.
@tc.returns(LabeledTensor)
@tc.accepts(LabeledTensorLike, tc.Mapping(string_types, tc.Union(int, slice)),
tc.Optional(string_types))
def slice_function(labeled_tensor, selection, name=None):
"""Slice out a subset of the tensor.
This is an analogue of tf.slice.
For example:
>>> tensor = tf.reshape(tf.range(0, 6), [3, 2])
>>> labeled_tensor = lt.LabeledTensor(tensor, ['a', ('b', ['foo', 'bar'])])
>>> lt.slice(labeled_tensor, {'a': slice(0, 2), 'b': 1})
<LabeledTensor 'lt_slice:...' shape=(2,) dtype=int32
axes=[('a', Dimension(2))]>
Args:
labeled_tensor: The input tensor.
selection: A dictionary of type str -> Union(int, slice of int) mapping
@property
def axes(self):
return self._axes
@property
def dtype(self):
return self._dtype
@property
def default_value(self):
return self._default_value
@tc.returns(tc.Dict(string_types, parsing_ops.FixedLenFeature))
@tc.accepts(tc.Mapping(string_types, FixedLenFeature))
def _labeled_to_unlabeled_features(features):
"""Convert a dict of lt.FixedLenFeature into a dict of tf.FixedLenFeature."""
unlabeled_features = {}
for name, labeled_feature in features.items():
shape = [ax.size for ax in labeled_feature.axes]
if any(size is None for size in shape):
# This should be caught on the TensorFlow side, but it isn't yet:
# https://github.com/tensorflow/tensorflow/issues/2874
raise ValueError('axes with unknown size are not supported')
dtype = labeled_feature.dtype
default_value = labeled_feature.default_value
unlabeled_features[name] = parsing_ops.FixedLenFeature(
shape, dtype, default_value)
return unlabeled_features
@tc.returns(core.LabeledTensor)
@tc.accepts(core.LabeledTensor, ops.Tensor, core.Axis,
tc.Optional(string_types))
def _gather_1d_on_axis(labeled_tensor, indexer, axis, name=None):
with ops.name_scope(name, 'lt_take', [labeled_tensor]) as scope:
temp_axes = core.Axes(
[axis] + list(labeled_tensor.axes.remove(axis.name).values()))
transposed = core.transpose(labeled_tensor, temp_axes.keys())
indexed = core.LabeledTensor(
array_ops.gather(transposed.tensor, indexer), temp_axes)
return core.transpose(indexed, labeled_tensor.axes.keys(), name=scope)
@tc.returns(core.LabeledTensor)
@tc.accepts(core.LabeledTensorLike,
tc.Mapping(string_types, tc.Union(slice,
collections.Hashable, list)),
tc.Optional(string_types))
def select(labeled_tensor, selection, name=None):
"""Slice out a subset of the tensor.
Args:
labeled_tensor: The input tensor.
selection: A dictionary mapping an axis name to a scalar, slice or list of
values to select. Currently supports two types of selections:
(a) Any number of scalar and/or slice selections.
(b) Exactly one list selection, without any scalars or slices.
name: Optional op name.
Returns:
The selection as a `LabeledTensor`.
Returns:
The tensor.
"""
with ops.name_scope(name, 'lt_identity', [labeled_tensor]) as scope:
labeled_tensor = convert_to_labeled_tensor(labeled_tensor)
return LabeledTensor(
array_ops.identity(
labeled_tensor.tensor, name=scope),
labeled_tensor.axes)
# We don't call this slice because that shadows a built-in. Instead, we alias
# this to lt.slice in __init__.py.
@tc.returns(LabeledTensor)
@tc.accepts(LabeledTensorLike,
tc.Mapping(string_types, tc.Union(int, slice)),
tc.Optional(string_types))
def slice_function(labeled_tensor, selection, name=None):
"""Slice out a subset of the tensor.
This is an analog of tf.slice.
For example:
>>> tensor = tf.reshape(tf.range(0, 6), [3, 2])
>>> labeled_tensor = lt.LabeledTensor(tensor, ['a', ('b', ['foo', 'bar'])])
>>> lt.slice(labeled_tensor, {'a': slice(0, 2), 'b': 1})
<LabeledTensor 'lt_slice:...' shape=(2,) dtype=int32
axes=[('a', Dimension(2))]>
Args:
labeled_tensor: The input tensor.
selection: A dictionary of type str -> Union(int, slice of int) mapping
