tf.TensorShape

Represents the shape of a Tensor.

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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]).