tf.TensorShape

Class TensorShape

Defined in tensorflow/python/framework/tensor_shape.py.

See the guide: Building Graphs > Defining new operations

Represents the shape of a Tensor.

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 in C++ for details of shape functions and how to register them. Alternatively, the shape may be set explicitly using tf.Tensor.set_shape.

Properties

dims

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

ndims

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

Methods

__init__

__init__(dims)

Creates a new TensorShape with the given dimensions.

Args:

• dims: A list of Dimensions, or None if the shape is unspecified.
• DEPRECATED: A single integer is treated as a singleton list.

Raises:

• TypeError: If dims cannot be converted to a list of dimensions.

__bool__

__bool__()

Returns True if this shape contains non-zero information.

__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:

A dimension if key is an integer, or a TensorShape if key is a slice.

Raises:

• ValueError: If key is a slice, and any of its elements are negative, or if 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.

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.

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

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.