tf.dtypes.DType

Represents the type of the elements in a Tensor.

Inherits From: TraceType

tf.dtypes.DType(
    type_enum, handle_data=None
)

DType's are used to specify the output data type for operations which require it, or to inspect the data type of existing Tensor's.

Examples:

tf.constant(1, dtype=tf.int64)
<tf.Tensor: shape=(), dtype=int64, numpy=1>
tf.constant(1.0).dtype
tf.float32

See tf.dtypes for a complete list of DType's defined.

as_datatype_enum Returns a types_pb2.DataType enum value based on this data type.
as_numpy_dtype Returns a Python type object based on this DType.
base_dtype Returns a non-reference DType based on this DType (for TF1).

Programs written for TensorFlow 2.x do not need this attribute. It exists only for compatibility with TensorFlow 1.x, which used reference DTypes in the implementation of tf.compat.v1.Variable. In TensorFlow 2.x, tf.Variable is implemented without reference types.

is_bool Returns whether this is a boolean data type.
is_complex Returns whether this is a complex floating point type.
is_floating Returns whether this is a (non-quantized, real) floating point type.
is_integer Returns whether this is a (non-quantized) integer type.
is_numeric Returns whether this is a numeric data type.
is_numpy_compatible Returns whether this data type has a compatible NumPy data type.
is_quantized Returns whether this is a quantized data type.
is_unsigned Returns whether this type is unsigned.

Non-numeric, unordered, and quantized types are not considered unsigned, and this function returns False.

limits Return intensity limits, i.e.

(min, max) tuple, of the dtype. Args: clip_negative : bool, optional If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. Returns min, max : tuple Lower and upper intensity limits.

max Returns the maximum representable value in this data type.
min Returns the minimum representable value in this data type.
name

real_dtype Returns the DType corresponding to this DType's real part.
size

Methods

experimental_as_proto

View source

experimental_as_proto() -> types_pb2.SerializedDType

Returns a proto representation of the Dtype instance.

experimental_from_proto

View source

@classmethod
experimental_from_proto(
    proto: types_pb2.SerializedDType
) -> 'DType'

Returns a Dtype instance based on the serialized proto.

experimental_type_proto

View source

@classmethod
experimental_type_proto() -> Type[types_pb2.SerializedDType]

Returns the type of proto associated with DType serialization.

is_compatible_with

View source

is_compatible_with(
    other
)

Returns True if the other DType will be converted to this DType (TF1).

Programs written for TensorFlow 2.x do not need this function. Instead, they can do equality comparison on DType objects directly: tf.as_dtype(this) == tf.as_dtype(other).

This function exists only for compatibility with TensorFlow 1.x, where it additionally allows conversion from a reference type (used by tf.compat.v1.Variable) to its base type.

Args
other A DType (or object that may be converted to a DType).

Returns
True if a Tensor of the other DType will be implicitly converted to this DType.

is_subtype_of

View source

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

See tf.types.experimental.TraceType base class.

most_specific_common_supertype

View source

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

See tf.types.experimental.TraceType base class.

__eq__

View source

__eq__(
    other
)

Returns True iff this DType refers to the same type as other.

__ne__

View source

__ne__(
    other
)

Returns True iff self != other.