Elementwise computes the bitwise XOR of `x` and `y`.
The result will have those bits set, that are different in `x` and `y`. The computation is performed on the underlying representations of `x` and `y`.
For example:
import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
tf.uint8, tf.uint16, tf.uint32, tf.uint64]
for dtype in dtype_list:
lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
exp = tf.constant([5, 5, 4, 5], dtype=tf.float32)
res = bitwise_ops.bitwise_xor(lhs, rhs)
tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of the tensor.
|
static <T extends TNumber> BitwiseXor<T> | |
Output<T> |
z()
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of the tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
public static BitwiseXor<T> create (Scope scope, Operand<T> x, Operand<T> y)
Factory method to create a class wrapping a new BitwiseXor operation.
Parameters
scope | current scope |
---|
Returns
- a new instance of BitwiseXor