tf.TensorShape

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

dims A list of Dimensions, or None if the shape is unspecified.

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

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

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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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Returns this shape as a TensorShapeProto.

assert_has_rank

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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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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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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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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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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.

is_compatible_with

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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

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Returns True iff self is fully defined in every dimension.

merge_with

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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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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

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

with_rank

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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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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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Returns a shape based on self with at most the given rank.

Args
rank An integer.

Returns