tf.TensorSpec

Describes a tf.Tensor.

Inherits From: TypeSpec, TraceType

tf.TensorSpec(
    shape,
    dtype=tf.dtypes.float32,
    name=None
)

Metadata for describing the tf.Tensor objects accepted or returned by some TensorFlow APIs.

shape Value convertible to tf.TensorShape. The shape of the tensor.
dtype Value convertible to tf.DType. The type of the tensor values.
name Optional name for the Tensor.

TypeError If shape is not convertible to a tf.TensorShape, or dtype is not convertible to a tf.DType.

dtype Returns the dtype of elements in the tensor.
name Returns the (optionally provided) name of the described tensor.
shape Returns the TensorShape that represents the shape of the tensor.
value_type The Python type for values that are compatible with this TypeSpec.

Methods

experimental_as_proto

View source

experimental_as_proto() -> struct_pb2.TensorSpecProto

Returns a proto representation of the TensorSpec instance.

experimental_from_proto

View source

@classmethod
experimental_from_proto(
    proto: struct_pb2.TensorSpecProto
) -> 'TensorSpec'

Returns a TensorSpec instance based on the serialized proto.

experimental_type_proto

View source

@classmethod
experimental_type_proto() -> Type[struct_pb2.TensorSpecProto]

Returns the type of proto associated with TensorSpec serialization.

from_spec

View source

@classmethod
from_spec(
    spec, name=None
)

Returns a TensorSpec with the same shape and dtype as spec.

spec = tf.TensorSpec(shape=[8, 3], dtype=tf.int32, name="OriginalName")
tf.TensorSpec.from_spec(spec, "NewName")
TensorSpec(shape=(8, 3), dtype=tf.int32, name='NewName')

Args
spec The TypeSpec used to create the new TensorSpec.
name The name for the new TensorSpec. Defaults to spec.name.

from_tensor

View source

@classmethod
from_tensor(
    tensor, name=None
)

Returns a TensorSpec that describes tensor.

tf.TensorSpec.from_tensor(tf.constant([1, 2, 3]))
TensorSpec(shape=(3,), dtype=tf.int32, name=None)

Args
tensor The tf.Tensor that should be described.
name A name for the TensorSpec. Defaults to tensor.op.name.

Returns
A TensorSpec that describes tensor.

is_compatible_with

View source

is_compatible_with(
    spec_or_tensor
)

Returns True if spec_or_tensor is compatible with this TensorSpec.

Two tensors are considered compatible if they have the same dtype and their shapes are compatible (see tf.TensorShape.is_compatible_with).

Args
spec_or_tensor A tf.TensorSpec or a tf.Tensor

Returns
True if spec_or_tensor is compatible with self.

is_subtype_of

View source

is_subtype_of(
    other: tf.types.experimental.TraceType
) -> bool

Returns True if self is a subtype of other.

Implements the tf.types.experimental.func.TraceType interface.

If not overridden by a subclass, the default behavior is to assume the TypeSpec is covariant upon attributes that implement TraceType and invariant upon rest of the attributes as well as the structure and type of the TypeSpec.

Args
other A TraceType object.

most_specific_common_supertype

View source

most_specific_common_supertype(
    others: Sequence[tf.types.experimental.TraceType]
) -> Optional['TypeSpec']

Returns the most specific supertype TypeSpec of self and others.

Implements the tf.types.experimental.func.TraceType interface.

If not overridden by a subclass, the default behavior is to assume the TypeSpec is covariant upon attributes that implement TraceType and invariant upon rest of the attributes as well as the structure and type of the TypeSpec.

Args
others A sequence of TraceTypes.

most_specific_compatible_type

View source

most_specific_compatible_type(
    other: 'TypeSpec'
) -> 'TypeSpec'

Returns the most specific TypeSpec compatible with self and other. (deprecated)

Deprecated. Please use most_specific_common_supertype instead. Do not override this function.

Args
other A TypeSpec.

Raises
ValueError If there is no TypeSpec that is compatible with both self and other.

__eq__

View source

__eq__(
    other
)

Return self==value.

__ne__

View source

__ne__(
    other
)

Return self!=value.