Tensor types

class tf.DType

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.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.

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.


tf.DType.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.


tf.DType.name

Returns the string name for this DType.


tf.DType.base_dtype

Returns a non-reference DType based on this DType.


tf.DType.real_dtype

Returns the dtype correspond to this dtype's real part.


tf.DType.is_floating

Returns whether this is a (non-quantized, real) floating point type.


tf.DType.is_complex

Returns whether this is a complex floating point type.


tf.DType.is_integer

Returns whether this is a (non-quantized) integer type.


tf.DType.is_quantized

Returns whether this is a quantized data type.


tf.DType.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.


tf.DType.as_numpy_dtype

Returns a numpy.dtype based on this DType.


tf.DType.as_datatype_enum

Returns a types_pb2.DataType enum value based on this DType.


tf.DType.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.

Other Methods


tf.DType.__eq__(other) {:#DType.eq}

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


tf.DType.__hash__() {:#DType.hash}


tf.DType.__init__(type_enum) {:#DType.init}

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.

tf.DType.__ne__(other) {:#DType.ne}

Returns True iff self != other.


tf.DType.__repr__() {:#DType.repr}


tf.DType.__str__() {:#DType.str}


tf.DType.is_numpy_compatible


tf.DType.max

Returns the maximum representable value in this data type.

Raises:
  • TypeError: if this is a non-numeric, unordered, or quantized type.

tf.DType.min

Returns the minimum representable value in this data type.

Raises:
  • TypeError: if this is a non-numeric, unordered, or quantized type.

tf.DType.size


tf.as_dtype(type_value)

Converts the given type_value to a DType.

Args:
  • type_value: A value that can be converted to a tf.DType object. This may currently be a tf.DType object, a DataType enum, a string type name, or a numpy.dtype.
Returns:

A DType corresponding to type_value.

Raises:
  • TypeError: If type_value cannot be converted to a DType.