# Class TensorSpec

Describes a tf.Tensor.

# Aliases:

• Class tf.compat.v1.TensorSpec
• Class tf.compat.v2.TensorSpec

# Used in the guide:

• Using the SavedModelformat

# Used in the tutorials:

• Betterperformancewith tf.function
• Transformer model for languageunderstanding`` tf.TensorMetadata for describing the objects accepted or returned by some TensorFlow APIs.

# init

View source

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

Creates a TensorSpec.

# Args:

• 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.

# Raises:

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

# Properties

# 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

# Methods

# eq

View source

 __eq__(other)

Return self==value.

# ne

View source

 __ne__(other)

Return self!=value.

# from_spec

View source

 @classmethod
from_spec(
    cls,
    spec,
    name=None
)

# from_tensor

View source

 @classmethod
from_tensor(
    cls,
    tensor,
    name=None
)

# is_compatible_with

View source

 is_compatible_with(spec_or_tensor)

Returns True if spec_or_tensor is compatible with this TensorSpec. tf.TensorShape.is_compatible_withTwo tensors are considered compatible if they have the same dtype and their shapes are compatible (see ).

# Args:

• spec_or_tensor: A tf.TensorSpec or a tf.Tensor

# Returns:

True if spec_or_tensor is compatible with self.

# most_specific_compatible_type

View source

 most_specific_compatible_type(other)

Returns the most specific TypeSpec compatible with self and other.

# Args:

• other: A TypeSpec.

# Raises:

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