tf.dtypes.DType
| View source on GitHub |
Represents the type of the elements in a Tensor.
tf.dtypes.DType()
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.
The tf.as_dtype() function converts numpy types and string type names to a DType object.
| Attributes | |
|---|---|
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. |
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 | 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 |
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
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
| 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. |
__eq__
__eq__(
other
)
Returns True iff this DType refers to the same type as other.
__ne__
__ne__(
other
)
Returns True iff self != other.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/dtypes/DType
