tf.math.logical_or

Returns the truth value of x OR y element-wise.

tf.math.logical_or(     x: Annotated[Any, tf.raw_ops.Any],     y: Annotated[Any, tf.raw_ops.Any],     name=None ) -> Annotated[Any, tf.raw_ops.Any]

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 type bool and one single bool, 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 type bool 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.

>>> 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.

x A tf.Tensor of type bool.
y A tf.Tensor of type bool.
name A name for the operation (optional).

A tf.Tensor of type bool with the shape that x and y broadcast to.

x A Tensor of type bool.
y A Tensor of type bool.
name A name for the operation (optional).

A Tensor of type bool.