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Method __init__

tensorflow/contrib/labeled_tensor/python/ops/core.py:74–111  ·  view source on GitHub ↗

Construct an Axis. Args: name: Name of the axis. value: Either None, an int or tf.compat.v1.Dimension giving the size of the axis, or a sequence that is not a string additionally providing coordinate (tick) labels. Raises: ValueError: If the user provides

(self, name, value)

Source from the content-addressed store, hash-verified

72
73 @tc.accepts(object, string_types, AxisValue)
74 def __init__(self, name, value):
75 """Construct an Axis.
76
77 Args:
78 name: Name of the axis.
79 value: Either None, an int or tf.compat.v1.Dimension giving the size of
80 the axis, or a sequence that is not a string additionally providing
81 coordinate (tick) labels.
82
83 Raises:
84 ValueError: If the user provides labels with duplicate values.
85 """
86 if isinstance(value, tensor_shape.Dimension):
87 dimension = value
88 labels = None
89 elif isinstance(value, int) or value is None:
90 dimension = tensor_shape.Dimension(value)
91 labels = None
92 else:
93 dimension = tensor_shape.Dimension(len(value))
94 labels = tuple(value)
95
96 if dimension.value == 0:
97 # Treat a zero-length axis as if it has labels.
98 labels = ()
99
100 if labels is not None:
101 index = dict(zip(labels, range(len(labels))))
102 if len(index) != len(labels):
103 raise ValueError(
104 'Tick labels must be unique, but got {}'.format(labels))
105 else:
106 index = None
107
108 self._name = name # type: string_types
109 self._dimension = dimension # type: tensor_shape.Dimension
110 self._labels = labels # type: Optional[tuple]
111 self._index = index # type: Optional[Dict[Any, int]]
112
113 @property
114 @tc.returns(string_types)

Callers

nothing calls this directly

Calls 4

tupleFunction · 0.85
rangeFunction · 0.50
DimensionMethod · 0.45
formatMethod · 0.45

Tested by

no test coverage detected