tf.TensorShape

TensorFlow 1 version

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Represents the shape of a Tensor.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.TensorShape

tf.TensorShape(
    dims
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate your TensorFlow 1 code to TensorFlow 2 Recurrent Neural Networks (RNN) with Keras	Distributed Input Text generation with an RNN

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

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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

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as_proto()

Returns this shape as a TensorShapeProto.

assert_has_rank

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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

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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

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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

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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

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concatenate(
    other
)

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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