tf.DType

Class DType

Defined in tensorflow/python/framework/dtypes.py.

See the guide: Building Graphs > Tensor types

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.

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

__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: A types_pb2.DataType enum value.

Raises:

• TypeError: If type_enum is not a value types_pb2.DataType.

__eq__

__eq__(other)

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

__int__

__int__()

__ne__

__ne__(other)

Returns True iff self != other.

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: 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.