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tf.bitcast

TensorFlow 2.0 version

Defined in generated file: python/ops/gen_array_ops.py

Bitcasts a tensor from one type to another without copying data.

Aliases:

  • tf.compat.v1.bitcast
  • tf.compat.v2.bitcast
tf.bitcast(
    input,
    type,
    name=None
)

Given a tensor input, this operation returns a tensor that has the same buffer data as input with datatype type.

If the input datatype T is larger than the output datatype type then the shape changes from [...] to [..., sizeof(T)/sizeof(type)].

If T is smaller than type, the operator requires that the rightmost dimension be equal to sizeof(type)/sizeof(T). The shape then goes from [..., sizeof(type)/sizeof(T)] to [...].

tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() gives module error. For example,

Example 1:

>>> a = [1., 2., 3.]
>>> equality_bitcast = tf.bitcast(a,tf.complex128)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot bitcast from float to complex128: shape [3] [Op:Bitcast]
>>> equality_cast = tf.cast(a,tf.complex128)
>>> print(equality_cast)
tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)

Example 2:

>>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
<tf.Tensor: ... shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>

Example 3:

>>> x = [1., 2., 3.]
>>> y = [0., 2., 3.]
>>> equality= tf.equal(x,y)
>>> equality_cast = tf.cast(equality,tf.float32)
>>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8)
>>> print(equality)
tf.Tensor([False True True], shape=(3,), dtype=bool)
>>> print(equality_cast)
tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
>>> print(equality_bitcast)
tf.Tensor(
[[ 0 0 0 0]
 [ 0 0 128 63]
 [ 0 0 128 63]], shape=(3, 4), dtype=uint8)

NOTE: Bitcast is implemented as a low-level cast, so machines with different endian orderings will give different results.

Args:

  • input: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
  • type: A tf.DType from: tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32.
  • name: A name for the operation (optional).

Returns:

A Tensor of type type.