tf.TensorShape

class tf.TensorShape

Properties

dims

ndims

Methods

__init__

__bool__

__eq__

__getitem__

__iter__

__len__

__ne__

__nonzero__

as_list

as_proto

assert_has_rank

assert_is_compatible_with

assert_is_fully_defined

assert_same_rank

concatenate

is_compatible_with

is_fully_defined

merge_with

num_elements

with_rank

with_rank_at_least

with_rank_at_most

`class tf.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.concatenate(other)

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.

`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.

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tf.TensorShape

`class tf.TensorShape`

Properties

`dims`

`ndims`

Methods

`init`

`bool`

`eq`

`getitem`

`iter`

`len`

`ne`

`nonzero`

`as_list`

`as_proto`

`assert_has_rank`

`assert_is_compatible_with`

`assert_is_fully_defined`

`assert_same_rank`

`concatenate`

`is_compatible_with`

`is_fully_defined`

`merge_with`

`num_elements`

`with_rank`

`with_rank_at_least`

`with_rank_at_most`

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