TensorFlow 1 version
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Represents the shape of a Tensor.
tf.TensorShape(
dims
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
A TensorShape represents a possibly-partial shape specification for a
Tensor. It may be one of the following:
- Fully-known shape: has a known number of dimensions and a known size
for each dimension. e.g.
TensorShape([16, 256]) - Partially-known shape: has a known number of dimensions, and an unknown
size for one or more dimension. e.g.
TensorShape([None, 256]) - Unknown shape: has an unknown number of dimensions, and an unknown
size in all dimensions. e.g.
TensorShape(None)
If a tensor is produced by an operation of type "Foo", its shape
may be inferred if there is a registered shape function for
"Foo". See Shape
functions
for details of shape functions and how to register them. Alternatively,
the shape may be set explicitly using tf.Tensor.set_shape.
Args:
dims: A list of Dimensions, or None if the shape is unspecified.
Attributes:
dims: Returns a list of Dimensions, or None if the shape is unspecified.ndims: Deprecated accessor forrank.rank: Returns the rank of this shape, or None if it is unspecified.
Raises:
TypeError: If dims cannot be converted to a list of dimensions.
Methods
__add__
__add__(
other
)
__bool__
__bool__()
Returns True if this shape contains non-zero information.
__concat__
__concat__(
other
)
__eq__
__eq__(
other
)
Returns True if self is equivalent to other.
__getitem__
__getitem__(
key
)
Returns the value of a dimension or a shape, depending on the key.
Args:
key: Ifkeyis an integer, returns the dimension at that index; otherwise ifkeyis a slice, returns a TensorShape whose dimensions are those selected by the slice fromself.
Returns:
An integer if key is an integer, or a TensorShape if key is a
slice.
Raises:
ValueError: Ifkeyis a slice andselfis completely unknown and the step is set.
__iter__
__iter__()
Returns self.dims if the rank is known, otherwise raises ValueError.
__len__
__len__()
Returns the rank of this shape, or raises ValueError if unspecified.
__ne__
__ne__(
other
)
Returns True if self is known to be different from other.
__nonzero__
__nonzero__()
Returns True if this shape contains non-zero information.
__radd__
__radd__(
other
)
as_list
as_list()
Returns a list of integers or None for each dimension.
Returns:
A list of integers or None for each dimension.
Raises:
ValueError: Ifselfis an unknown shape with an unknown rank.
as_proto
as_proto()
Returns this shape as a TensorShapeProto.
assert_has_rank
assert_has_rank(
rank
)
Raises an exception if self is not compatible with the given rank.
Args:
rank: An integer.
Raises:
ValueError: Ifselfdoes not represent a shape with the givenrank.
assert_is_compatible_with
assert_is_compatible_with(
other
)
Raises exception if self and other do not represent the same shape.
This method can be used to assert that there exists a shape that both
self and other represent.
Args:
other: Another TensorShape.
Raises:
ValueError: Ifselfandotherdo not represent the same shape.
assert_is_fully_defined
assert_is_fully_defined()
Raises an exception if self is not fully defined in every dimension.
Raises:
ValueError: Ifselfdoes not have a known value for every dimension.
assert_same_rank
assert_same_rank(
other
)
Raises an exception if self and other do not have compatible ranks.
Args:
other: AnotherTensorShape.
Raises:
ValueError: Ifselfandotherdo not represent shapes with the same rank.
concatenate
concatenate(
other
)
Returns the concatenation of the dimension in self and other.
N.B. If either self or other is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
information for use with slicing.
Args:
other: AnotherTensorShape.
Returns:
A TensorShape whose dimensions are the concatenation of the
dimensions in self and other.
is_compatible_with
is_compatible_with(
other
)
Returns True iff self is compatible with other.
Two possibly-partially-defined shapes are compatible if there exists a fully-defined shape that both shapes can represent. Thus, compatibility allows the shape inference code to reason about partially-defined shapes. For example:
TensorShape(None) is compatible with all shapes.
TensorShape([None, None]) is compatible with all two-dimensional shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is not compatible with, for example, TensorShape([None]) or TensorShape([None, None, None]).
TensorShape([32, None]) is compatible with all two-dimensional shapes with size 32 in the 0th dimension, and also TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
TensorShape([32, 784]) is compatible with itself, and also TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32, 1, 784]) or TensorShape([None]).
The compatibility relation is reflexive and symmetric, but not transitive. For example, TensorShape([32, 784]) is compatible with TensorShape(None), and TensorShape(None) is compatible with TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with TensorShape([4, 4]).
Args:
other: Another TensorShape.
Returns:
True iff self is compatible with other.
is_fully_defined
is_fully_defined()
Returns True iff self is fully defined in every dimension.
merge_with
merge_with(
other
)
Returns a TensorShape combining the information in self and other.
The dimensions in self and other are merged elementwise,
according to the rules defined for Dimension.merge_with().
Args:
other: AnotherTensorShape.
Returns:
A TensorShape containing the combined information of self and
other.
Raises:
ValueError: Ifselfandotherare not compatible.
most_specific_compatible_shape
most_specific_compatible_shape(
other
)
Returns the most specific TensorShape compatible with self and other.
TensorShape([None, 1]) is the most specific TensorShape compatible with both TensorShape([2, 1]) and TensorShape([5, 1]). Note that TensorShape(None) is also compatible with above mentioned TensorShapes.
TensorShape([1, 2, 3]) is the most specific TensorShape compatible with both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more less specific TensorShapes compatible with above mentioned TensorShapes, e.g. TensorShape([1, 2, None]), TensorShape(None).
Args:
other: AnotherTensorShape.
Returns:
A TensorShape which is the most specific compatible shape of self
and other.
num_elements
num_elements()
Returns the total number of elements, or none for incomplete shapes.
with_rank
with_rank(
rank
)
Returns a shape based on self with the given rank.
This method promotes a completely unknown shape to one with a known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as self with the given rank.
Raises:
ValueError: Ifselfdoes not represent a shape with the givenrank.
with_rank_at_least
with_rank_at_least(
rank
)
Returns a shape based on self with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as self with at least the given
rank.
Raises:
ValueError: Ifselfdoes not represent a shape with at least the givenrank.
with_rank_at_most
with_rank_at_most(
rank
)
Returns a shape based on self with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as self with at most the given
rank.
Raises:
ValueError: Ifselfdoes not represent a shape with at most the givenrank.