# tf.TensorShape

Represents the shape of a `Tensor`.

Inherits From: `TraceType`

````t = tf.constant([[1,2,3],[4,5,6]])`
`t.shape`
`TensorShape([2, 3])`
```

`TensorShape` is the static shape representation of a Tensor. During eager execution a Tensor always has a fully specified shape but when tracing a `tf.function` 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)`

During function tracing `t.shape` will return a `TensorShape` object representing the shape of Tensor as it is known during tracing. This static representation will be partially defined in cases where the exact shape depends on the values within the tensors. To get the dynamic representation, please use `tf.shape(t)` which will return Tensor representing the fully defined shape of `t`. This way, you can express logic that manipulates the shapes of tensors by building other tensors that depend on the dynamic shape of `t`.

For example, this function prints the `TensorShape' (`t.shape```), when you trace the function, and returns a tensor <a href="../tf/shape"><code>tf.shape(t)</code></a> for given input```t`:

````@tf.function`
`def get_dynamic_shape(t):`
`  print("tracing...")`
`  print(f"static shape is {t.shape}")`
`  return tf.shape(t)`
```

Just calling the function traces it with a fully-specified static shape:

````result = get_dynamic_shape(tf.constant([[1, 1, 1], [0, 0, 0]]))`
`tracing...`
`static shape is (2, 3)`
`result.numpy()`
`array([2, 3], dtype=int32)`
```

But `tf.function` can also trace the function with a partially specified (or even unspecified) shape:

````cf1 = get_dynamic_shape.get_concrete_function(tf.TensorSpec(`
`                                              shape=[None, 2]))`
`tracing...`
`static shape is (None, 2)`
`cf1(tf.constant([[1., 0],[1, 0],[1, 0]])).numpy()`
`array([3, 2], dtype=int32)`
```
````cf2 = get_dynamic_shape.get_concrete_function(tf.TensorSpec(shape=None))`
`tracing...`
`static shape is <unknown>`
`cf2(tf.constant([[[[[1., 0]]]]])).numpy()`
`array([1, 1, 1, 1, 2], dtype=int32)`
```

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, you may set the shape explicitly using `tf.Tensor.ensure_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`

View source

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`.

### `experimental_as_proto`

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Returns a proto representation of the TensorShape instance.

### `experimental_from_proto`

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Returns a TensorShape instance based on the serialized proto.

### `experimental_type_proto`

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Returns the type of proto associated with TensorShape serialization.

### `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.

### `is_subtype_of`

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Returns True iff `self` is subtype of `other`.

Shape A is a subtype of shape B if shape B can successfully represent it:

• A `TensorShape` of any rank is a subtype of `TensorShape(None)`.

• TensorShapes of equal ranks are covariant, i.e. `TensorShape([A1, A2, ..])` is a subtype of `TensorShape([B1, B2, ..])` iff An is a subtype of Bn.

An is subtype of Bn iff An == Bn or Bn is None.

• TensorShapes of different defined ranks have no subtyping relation.

The subtyping relation is reflexive and transitive, but not symmetric.

#### Some examples:

• `TensorShape([32, 784])` is a subtype of `TensorShape(None)`, and `TensorShape([4, 4])` is also a subtype of `TensorShape(None)` but `TensorShape([32, 784])` and `TensorShape([4, 4])` are not subtypes of each other.

• All two-dimensional shapes are subtypes of `TensorShape([None, None])`, such as `TensorShape([32, 784])`. There is no subtype relationship with, for example, `TensorShape([None])` or `TensorShape([None, None, None])`.

• `TensorShape([32, None])` is also a subtype of `TensorShape([None, None])` and `TensorShape(None)`. It is not a subtype of, for example, `TensorShape([32])`, `TensorShape([32, None, 1])`, `TensorShape([64, None])` or `TensorShape([None, 32])`.

• `TensorShape([32, 784])` is a subtype of itself, and also `TensorShape([32, None])`, `TensorShape([None, 784])`, `TensorShape([None, None])` and `TensorShape(None)`. It has no subtype relation with, for example, `TensorShape([32, 1, 784])` or `TensorShape([None])`.

Args
`other` Another `TensorShape`.

Returns
True iff `self` is subtype of `other`.

### `merge_with`

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Returns a `TensorShape` combining the information in `self` and `other`.

The dimensions in `self` and `other` are merged element-wise, according to the rules below:

``````Dimension(n).merge_with(Dimension(None)) == Dimension(n)
Dimension(None).merge_with(Dimension(n)) == Dimension(n)
Dimension(None).merge_with(Dimension(None)) == Dimension(None)
# raises ValueError for n != m
Dimension(n).merge_with(Dimension(m))
``````

ts = tf.TensorShape([1,2]) ot1 = tf.TensorShape([1,2]) ts.merge_with(ot).as_list() [1,2]

ot2 = tf.TensorShape([1,None]) ts.merge_with(ot2).as_list() [1,2]

ot3 = tf.TensorShape([None, None]) ot3.merge_with(ot2).as_list() [1, None]

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_common_supertype`

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Returns the most specific supertype `TensorShape` of self and others.

• `TensorShape([None, 1])` is the most specific `TensorShape` supertyping both `TensorShape([2, 1])` and `TensorShape([5, 1])`. Note that `TensorShape(None)` is also a supertype but it is not "most specific".

• `TensorShape([1, 2, 3])` is the most specific `TensorShape` supertyping both `TensorShape([1, 2, 3])` and `TensorShape([1, 2, 3]`). There are other less specific TensorShapes that supertype above mentioned TensorShapes, e.g. `TensorShape([1, 2, None])`, `TensorShape(None)`.

• `TensorShape([None, None])` is the most specific `TensorShape` supertyping both `TensorShape([2, None])` and `TensorShape([None, 3])`. As always, `TensorShape(None)` is also a supertype but not the most specific one.

• `TensorShape(None`) is the only `TensorShape` supertyping both `TensorShape([1, 2, 3])` and `TensorShape([1, 2])`. In general, any two shapes that have different ranks will only have `TensorShape(None)` as a common supertype.

• `TensorShape(None)` is the only `TensorShape` supertyping both `TensorShape([1, 2, 3])` and `TensorShape(None)`. In general, the common supertype of any shape with `TensorShape(None)` is `TensorShape(None)`.

Args
`others` Sequence of `TensorShape`.

Returns
A `TensorShape` which is the most specific supertype shape of `self` and `others`. None if it does not exist.

### `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
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`.

View source

### `__bool__`

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Returns True if this shape contains non-zero information.

View source

### `__eq__`

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Returns True if `self` is equivalent to `other`.

It first tries to convert `other` to `TensorShape`. `TypeError` is thrown when the conversion fails. Otherwise, it compares each element in the TensorShape dimensions.

• Two Fully known shapes, return True iff each element is equal.
``````>>> t_a = tf.TensorShape([1,2])
>>> a = [1, 2]
>>> t_b = tf.TensorShape([1,2])
>>> t_c = tf.TensorShape([1,2,3])
>>> t_a.__eq__(a)
True
>>> t_a.__eq__(t_b)
True
>>> t_a.__eq__(t_c)
False
``````
• Two Partially-known shapes, return True iff each element is equal.
``````>>> p_a = tf.TensorShape([1,None])
>>> p_b = tf.TensorShape([1,None])
>>> p_c = tf.TensorShape([2,None])
>>> p_a.__eq__(p_b)
True
>>> t_a.__eq__(p_a)
False
>>> p_a.__eq__(p_c)
False
``````
• Two Unknown shape, return True.
``````>>> unk_a = tf.TensorShape(None)
>>> unk_b = tf.TensorShape(None)
>>> unk_a.__eq__(unk_b)
True
>>> unk_a.__eq__(t_a)
False
``````

Args
`other` A `TensorShape` or type that can be converted to `TensorShape`.

Returns
True if the dimensions are all equal.

Raises
TypeError if `other` can not be converted to `TensorShape`.

### `__getitem__`

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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__`

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Returns `self.dims` if the rank is known, otherwise raises ValueError.

### `__len__`

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Returns the rank of this shape, or raises ValueError if unspecified.

### `__nonzero__`

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Returns True if this shape contains non-zero information.

### `__radd__`

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[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]
{ "last_modified": "Last updated 2024-01-23 UTC.", "state": "" }