# Class TensorShape

Represents the shape of a Tensor.

# Aliases:

• Class tf.compat.v1.TensorShape
• Class tf.compat.v2.TensorShape

# Used in the guide:

• Migrate yourTensorFlow 1 codeto TensorFlow2``
• Recurrent NeuralNetworks (RNN) withKeras``

# Used in the tutorials:

• Textgeneration withan RNN 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) Shape functionsIf 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 for details of shape functions and how to register them. Alternatively, the shape may be set explicitly using tf.Tensor.set_shape.

# init

View source

 __init__(dims)

Creates a new TensorShape with the given dimensions.

# 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.

# Properties

# dims

Returns a list of Dimensions, or None if the shape is unspecified.

# ndims

Deprecated accessor for rank.

# rank

Returns the rank of this shape, or None if it is unspecified.

# Methods

# add

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 __add__(other)

# bool

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 __bool__()

Returns True if this shape contains non-zero information.

# concat

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 __concat__(other)

# eq

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 __eq__(other)

Returns True if self is equivalent to other.

# getitem

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 __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

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 __iter__()

Returns self.dims if the rank is known, otherwise raises ValueError.

# len

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 __len__()

Returns the rank of this shape, or raises ValueError if unspecified.

# ne

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 __ne__(other)

Returns True if self is known to be different from other.

# nonzero

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 __nonzero__()

Returns True if this shape contains non-zero information.

# radd

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 __radd__(other)

# as_list

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 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

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 as_proto()

Returns this shape as a TensorShapeProto.

# assert_has_rank

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 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

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 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

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 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

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 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

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 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: Another TensorShape.

# Returns:

A TensorShape whose dimensions are the concatenation of the dimensions in self and other.

# is_compatible_with

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 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) iscompatible withallshapes.``
• TensorShape([None,None]) iscompatible withalltwo-dimensionalshapes,such asTensorShape([32, 784]),andalso TensorShape(None).It isnotcompatible with, for example,TensorShape([None])or TensorShape([None,None,None]).``
• TensorShape([32,None]) iscompatible withalltwo-dimensionalshapes withsize 32in the 0th dimension,andalso TensorShape([None,None]) and TensorShape(None).It isnotcompatible with, for example,TensorShape([32]), TensorShape([32,None,1])or TensorShape([64,None]).``
• TensorShape([32,784])is compatiblewith itself, and alsoTensorShape([32, None]), TensorShape([None,784]), TensorShape([None,None]) and TensorShape(None).It isnotcompatible 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

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 is_fully_defined()

Returns True iff self is fully defined in every dimension.

# merge_with

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 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

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 most_specific_compatible_shape(other)

Returns the most specific TensorShape compatible with self and other.

• TensorShape([None,1])is the mostspecific TensorShape compatiblewith bothTensorShape([2,1])andTensorShape([5,1]). Notethat TensorShape(None) isalso compatiblewith above mentioned TensorShapes.
• TensorShape([1, 2,3])is the mostspecific TensorShape compatiblewith bothTensorShape([1,2, 3]) and TensorShape([1, 2,3]). There are moreless specificTensorShapes compatiblewith 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

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 num_elements()

Returns the total number of elements, or none for incomplete shapes.

# with_rank

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 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

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 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

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 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.