tf.TensorShape

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Represents the shape of a Tensor.

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Compat aliases for migration

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tf.compat.v1.TensorShape

tf.TensorShape(
    dims
)

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 `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 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 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`	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/r2.3/api_docs/python/tf/TensorShape