tf.TensorShape | TensorFlow v2.16.1 (original) (raw)
Represents the shape of a Tensor.
Inherits From: TraceType
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.TensorShape(
dims
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
| TensorFlow 1.x vs TensorFlow 2 - Behaviors and APIs DTensor concepts Working with RNNs | Distributed Input Tensorflow datasets from MongoDB collections |
t = tf.constant([[1,2,3],[4,5,6]])
t.shape
TensorShape([2, 3])
TensorShape is the static shape representation of a Tensor. During eager execution a Tensor always has a fully specified shape but when tracing a tf.function 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)
During function tracing t.shape will return a TensorShape object representing the shape of Tensor as it is known during tracing. This static representation will be partially defined in cases where the exact shape depends on the values within the tensors. To get the_dynamic_ representation, please use tf.shape(t)which will return Tensor representing the fully defined shape of t. This way, you can express logic that manipulates the shapes of tensors by building other tensors that depend on the dynamic shape of t.
For example, this function prints the TensorShape' (t.shape), when you trace the function, and returns a tensor <a href="../tf/shape"><code>tf.shape(t)</code></a> for given inputt`:
@tf.function
def get_dynamic_shape(t):
print("tracing...")
print(f"static shape is {t.shape}")
return tf.shape(t)
Just calling the function traces it with a fully-specified static shape:
result = get_dynamic_shape(tf.constant([[1, 1, 1], [0, 0, 0]]))
tracing...
static shape is (2, 3)
result.numpy()
array([2, 3], dtype=int32)
But tf.function can also trace the function with a partially specified (or even unspecified) shape:
cf1 = get_dynamic_shape.get_concrete_function(tf.TensorSpec(
shape=[None, 2]))
tracing...
static shape is (None, 2)
cf1(tf.constant([[1., 0],[1, 0],[1, 0]])).numpy()
array([3, 2], dtype=int32)
cf2 = get_dynamic_shape.get_concrete_function(tf.TensorSpec(shape=None))
tracing...
static shape is <unknown>
cf2(tf.constant([[[[[1., 0]]]]])).numpy()
array([1, 1, 1, 1, 2], dtype=int32)
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 functionsfor details of shape functions and how to register them. Alternatively, you may set the shape explicitly using tf.Tensor.ensure_shape.
| Args | |
|---|---|
| dims | A list of Dimensions, or None if the shape is unspecified. |
| Raises | |
|---|---|
| TypeError | If dims cannot be converted to a list of dimensions. |
| Attributes | |
|---|---|
| dims | Deprecated. Returns list of dimensions for this shape.Suggest TensorShape.as_list instead. |
| ndims | Deprecated accessor for rank. |
| rank | Returns the rank of this shape, or None if it is unspecified. |
Methods
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 | If self is 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 | If self does not represent a shape with the given rank. |
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 bothself and other represent.
| Args | |
|---|---|
| other | Another TensorShape. |
| Raises | |
|---|---|
| ValueError | If self and other do 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 | If self does 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 | Another TensorShape. |
| Raises | |
|---|---|
| ValueError | If self and other do not represent shapes with the same rank. |
concatenate
concatenate(
other
)
Returns the concatenation of the dimension in self and other.
| Args | |
|---|---|
| other | Another TensorShape. |
| Returns |
|---|
| A TensorShape whose dimensions are the concatenation of the dimensions in self and other. |
experimental_as_proto
experimental_as_proto() -> tensor_shape_pb2.TensorShapeProto
Returns a proto representation of the TensorShape instance.
experimental_from_proto
@classmethod
experimental_from_proto( proto: tensor_shape_pb2.TensorShapeProto ) -> 'TensorShape'
Returns a TensorShape instance based on the serialized proto.
experimental_type_proto
@classmethod
experimental_type_proto() -> Type[tensor_shape_pb2.TensorShapeProto]
Returns the type of proto associated with TensorShape serialization.
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.
is_subtype_of
is_subtype_of(
other: tf.types.experimental.TraceType
) -> bool
Returns True iff self is subtype of other.
Shape A is a subtype of shape B if shape B can successfully represent it:
- A
TensorShapeof any rank is a subtype ofTensorShape(None). - TensorShapes of equal ranks are covariant, i.e.
TensorShape([A1, A2, ..])is a subtype ofTensorShape([B1, B2, ..])iff An is a subtype of Bn.
An is subtype of Bn iff An == Bn or Bn is None. - TensorShapes of different defined ranks have no subtyping relation.
The subtyping relation is reflexive and transitive, but not symmetric.
Some examples:
TensorShape([32, 784])is a subtype ofTensorShape(None), andTensorShape([4, 4])is also a subtype ofTensorShape(None)butTensorShape([32, 784])andTensorShape([4, 4])are not subtypes of each other.- All two-dimensional shapes are subtypes of
TensorShape([None, None]), such asTensorShape([32, 784]). There is no subtype relationship with, for example,TensorShape([None])orTensorShape([None, None, None]). TensorShape([32, None])is also a subtype ofTensorShape([None, None])andTensorShape(None). It is not a subtype of, for example,TensorShape([32]),TensorShape([32, None, 1]),TensorShape([64, None])orTensorShape([None, 32]).TensorShape([32, 784])is a subtype of itself, and alsoTensorShape([32, None]),TensorShape([None, 784]),TensorShape([None, None])andTensorShape(None). It has no subtype relation with, for example,TensorShape([32, 1, 784])orTensorShape([None]).
| Args | |
|---|---|
| other | Another TensorShape. |
| Returns |
|---|
| True iff self is subtype of other. |
merge_with
merge_with(
other
)
Returns a TensorShape combining the information in self and other.
The dimensions in self and other are merged element-wise, according to the rules below:
Dimension(n).merge_with(Dimension(None)) == Dimension(n)
Dimension(None).merge_with(Dimension(n)) == Dimension(n)
Dimension(None).merge_with(Dimension(None)) == Dimension(None)
# raises ValueError for n != m
Dimension(n).merge_with(Dimension(m))
ts = tf.TensorShape([1,2]) ot1 = tf.TensorShape([1,2]) ts.merge_with(ot).as_list() [1,2]
ot2 = tf.TensorShape([1,None]) ts.merge_with(ot2).as_list() [1,2]
ot3 = tf.TensorShape([None, None]) ot3.merge_with(ot2).as_list() [1, None]
| Args | |
|---|---|
| other | Another TensorShape. |
| Returns |
|---|
| A TensorShape containing the combined information of self andother. |
| Raises | |
|---|---|
| ValueError | If self and other are not compatible. |
most_specific_common_supertype
most_specific_common_supertype(
others: Sequence[tf.types.experimental.TraceType]
) -> Optional['TensorShape']
Returns the most specific supertype TensorShape of self and others.
TensorShape([None, 1])is the most specificTensorShapesupertyping bothTensorShape([2, 1])andTensorShape([5, 1]). Note thatTensorShape(None)is also a supertype but it is not "most specific".TensorShape([1, 2, 3])is the most specificTensorShapesupertyping bothTensorShape([1, 2, 3])andTensorShape([1, 2, 3]). There are other less specific TensorShapes that supertype above mentioned TensorShapes, e.g.TensorShape([1, 2, None]),TensorShape(None).TensorShape([None, None])is the most specificTensorShapesupertyping bothTensorShape([2, None])andTensorShape([None, 3]). As always,TensorShape(None)is also a supertype but not the most specific one.TensorShape(None) is the onlyTensorShapesupertyping bothTensorShape([1, 2, 3])andTensorShape([1, 2]). In general, any two shapes that have different ranks will only haveTensorShape(None)as a common supertype.TensorShape(None)is the onlyTensorShapesupertyping bothTensorShape([1, 2, 3])andTensorShape(None). In general, the common supertype of any shape withTensorShape(None)isTensorShape(None).
| Args | |
|---|---|
| others | Sequence of TensorShape. |
| Returns |
|---|
| A TensorShape which is the most specific supertype shape of selfand others. None if it does not exist. |
most_specific_compatible_shape
most_specific_compatible_shape(
other
) -> 'TensorShape'
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 | Another TensorShape. |
| Returns |
|---|
| A TensorShape which is the most specific compatible shape of selfand 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 | If self does not represent a shape with the given rank. |
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 | If self does 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 | If self does not represent a shape with at most the givenrank. |
__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.
It first tries to convert other to TensorShape. TypeError is thrown when the conversion fails. Otherwise, it compares each element in the TensorShape dimensions.
- Two Fully known shapes, return True iff each element is equal.
>>> t_a = tf.TensorShape([1,2])
>>> a = [1, 2]
>>> t_b = tf.TensorShape([1,2])
>>> t_c = tf.TensorShape([1,2,3])
>>> t_a.__eq__(a)
True
>>> t_a.__eq__(t_b)
True
>>> t_a.__eq__(t_c)
False
- Two Partially-known shapes, return True iff each element is equal.
>>> p_a = tf.TensorShape([1,None])
>>> p_b = tf.TensorShape([1,None])
>>> p_c = tf.TensorShape([2,None])
>>> p_a.__eq__(p_b)
True
>>> t_a.__eq__(p_a)
False
>>> p_a.__eq__(p_c)
False
- Two Unknown shape, return True.
>>> unk_a = tf.TensorShape(None)
>>> unk_b = tf.TensorShape(None)
>>> unk_a.__eq__(unk_b)
True
>>> unk_a.__eq__(t_a)
False
| Args | |
|---|---|
| other | A TensorShape or type that can be converted to TensorShape. |
| Returns |
|---|
| True if the dimensions are all equal. |
| Raises |
|---|
| TypeError if other can not be converted to TensorShape. |
__getitem__
__getitem__(
key
)
Returns the value of a dimension or a shape, depending on the key.
| Args | |
|---|---|
| key | If key is an integer, returns the dimension at that index; otherwise if key is a slice, returns a TensorShape whose dimensions are those selected by the slice from self. |
| Returns |
|---|
| An integer if key is an integer, or a TensorShape if key is a slice. |
| Raises | |
|---|---|
| ValueError | If key is a slice and self is 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.
__nonzero__
__nonzero__()
Returns True if this shape contains non-zero information.
__radd__
__radd__(
other
)