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Represents the shape of a Tensor
.
tf.TensorShape( dims )
A TensorShape
represents a possibly-partial shape specification for a Tensor
. It may be one of the following:
TensorShape([16, 256])
TensorShape([None, 256])
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. |
Raises | |
---|---|
TypeError | If dims cannot be converted to a list of dimensions. |
Attributes | |
---|---|
dims | Deprecated. Returns list of dimensions for this shape. Suggest |
ndims | Deprecated accessor for rank . |
rank | Returns the rank of this shape, or None if it is unspecified. |
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 both self
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
.
Note: If eitherself
orother
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 | Another TensorShape . |
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 | Another TensorShape . |
Returns | |
---|---|
A TensorShape containing the combined information of self and other . |
Raises | |
---|---|
ValueError | If self and other are 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 | Another TensorShape . |
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 | 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 given rank . |
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 given rank . |
__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 | 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.
__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 )
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/TensorShape