TensorFlow 1 version
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View source on GitHub
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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. |
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. |
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.
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| Raises | |
|---|---|
ValueError
|
If self is an unknown shape with an unknown rank.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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| Raises | |
|---|---|
ValueError
|
If self does not represent a shape with the given rank.
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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.
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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.
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__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.
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| Raises | |
|---|---|
ValueError
|
If key is a slice and self is completely unknown and
the step is set.
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__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
)