TensorFlow 1 version |
Returns the truth value of x OR y element-wise.
tf.math.logical_or(
x, y, name=None
)
Logical OR function.
Requires that x
and y
have the same shape or have
broadcast-compatible
shapes. For example, x
and y
can be:
- Two single elements of type
bool
. - One
tf.Tensor
of typebool
and one singlebool
, where the result will be calculated by applying logical OR with the single element to each element in the larger Tensor. - Two
tf.Tensor
objects of typebool
of the same shape. In this case, the result will be the element-wise logical OR of the two input tensors.
You can also use the |
operator instead.
Usage:
a = tf.constant([True])
b = tf.constant([False])
tf.math.logical_or(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
a | b
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
c = tf.constant([False])
x = tf.constant([False, True, True, False])
tf.math.logical_or(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
c | x
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
y = tf.constant([False, False, True, True])
z = tf.constant([False, True, False, True])
tf.math.logical_or(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>
y | z
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>
This op also supports broadcasting
tf.logical_or([[True, False]], [[True], [False]])
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[ True, True],
[ True, False]])>
The reduction version of this elementwise operation is tf.math.reduce_any
.
Args | |
---|---|
x
|
A tf.Tensor of type bool.
|
y
|
A tf.Tensor of type bool.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A tf.Tensor of type bool with the shape that x and y broadcast to.
|
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|