Bitcast

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

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) Traceback (most recent call last): ... InvalidArgumentError: Cannot bitcast from 1 to 18 [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)

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.