Class DType
Aliases:
- Class
tf.DType
- Class
tf.dtypes.DType
Defined in tensorflow/python/framework/dtypes.py
.
Represents the type of the elements in a Tensor
.
The following DType
objects are defined:
tf.float16
: 16-bit half-precision floating-point.tf.float32
: 32-bit single-precision floating-point.tf.float64
: 64-bit double-precision floating-point.tf.bfloat16
: 16-bit truncated floating-point.tf.complex64
: 64-bit single-precision complex.tf.complex128
: 128-bit double-precision complex.tf.int8
: 8-bit signed integer.tf.uint8
: 8-bit unsigned integer.tf.uint16
: 16-bit unsigned integer.tf.uint32
: 32-bit unsigned integer.tf.uint64
: 64-bit unsigned integer.tf.int16
: 16-bit signed integer.tf.int32
: 32-bit signed integer.tf.int64
: 64-bit signed integer.tf.bool
: Boolean.tf.string
: String.tf.qint8
: Quantized 8-bit signed integer.tf.quint8
: Quantized 8-bit unsigned integer.tf.qint16
: Quantized 16-bit signed integer.tf.quint16
: Quantized 16-bit unsigned integer.tf.qint32
: Quantized 32-bit signed integer.tf.resource
: Handle to a mutable resource.tf.variant
: Values of arbitrary types.
In addition, variants of these types with the _ref
suffix are
defined for reference-typed tensors.
The tf.as_dtype()
function converts numpy types and string type
names to a DType
object.
__init__
__init__(type_enum)
Creates a new DataType
.
NOTE(mrry): In normal circumstances, you should not need to
construct a DataType
object directly. Instead, use the
tf.as_dtype()
function.
Args:
type_enum
: Atypes_pb2.DataType
enum value.
Raises:
TypeError
: Iftype_enum
is not a valuetypes_pb2.DataType
.
Properties
as_datatype_enum
Returns a types_pb2.DataType
enum value based on this DType
.
as_numpy_dtype
Returns a numpy.dtype
based on this DType
.
base_dtype
Returns a non-reference DType
based on this DType
.
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_numpy_compatible
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
.
Returns:
Whether a DType
is unsigned.
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.
Raises:
TypeError
: if this is a non-numeric, unordered, or quantized type.
min
Returns the minimum representable value in this data type.
Raises:
TypeError
: if this is a non-numeric, unordered, or quantized type.
name
Returns the string name for this DType
.
real_dtype
Returns the dtype correspond to this dtype's real part.
size
Methods
tf.dtypes.DType.__eq__
__eq__(other)
Returns True iff this DType refers to the same type as other
.
tf.dtypes.DType.__int__
__int__()
tf.dtypes.DType.__ne__
__ne__(other)
Returns True iff self != other.
tf.dtypes.DType.is_compatible_with
is_compatible_with(other)
Returns True if the other
DType will be converted to this DType.
The conversion rules are as follows:
DType(T) .is_compatible_with(DType(T)) == True
DType(T) .is_compatible_with(DType(T).as_ref) == True
DType(T).as_ref.is_compatible_with(DType(T)) == False
DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
Args:
other
: ADType
(or object that may be converted to aDType
).
Returns:
True if a Tensor of the other
DType
will be implicitly converted to
this DType
.