tf.TensorShape     View source on GitHub    Represents the shape of a Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.TensorShape  
tf.TensorShape(
    dims
)
 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.
 


 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.   
 


 Attributes
  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 View source 
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 View source 
as_proto()
 Returns this shape as a TensorShapeProto. assert_has_rank View source 
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 View source 
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 View source 
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 View source 
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 View source 
concatenate(
    other
)
 Returns the concatenation of the dimension in self and other. 
Note: 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 View source 
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 View source 
is_fully_defined()
 Returns True iff self is fully defined in every dimension. merge_with View source 
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 View source 
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 View source 
num_elements()
 Returns the total number of elements, or none for incomplete shapes. with_rank View source 
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 View source 
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 View source 
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.    __add__ View source 
__add__(
    other
)
 __bool__ View source 
__bool__()
 Returns True if this shape contains non-zero information. __concat__ View source 
__concat__(
    other
)
 __eq__ View source 
__eq__(
    other
)
 Returns True if self is equivalent to other. __getitem__ View source 
__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__ View source 
__iter__()
 Returns self.dims if the rank is known, otherwise raises ValueError. __len__ View source 
__len__()
 Returns the rank of this shape, or raises ValueError if unspecified. __ne__ View source 
__ne__(
    other
)
 Returns True if self is known to be different from other. __nonzero__ View source 
__nonzero__()
 Returns True if this shape contains non-zero information. __radd__ View source 
__radd__(
    other
)