tf.math.logical_and
Returns the truth value of x AND y element-wise.
tf.math.logical_and( x, y, name=None )
Logical AND 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 AND 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 AND of the two input tensors.
You can also use the &
operator instead.
Usage:
a = tf.constant([True]) b = tf.constant([False]) tf.math.logical_and(a, b) <tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])> a & b <tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
c = tf.constant([True]) x = tf.constant([False, True, True, False]) tf.math.logical_and(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_and(y, z) <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])> y & z <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>
This op also supports broadcasting
tf.logical_and([[True, False]], [[True], [False]]) <tf.Tensor: shape=(2, 2), dtype=bool, numpy= array([[ True, False], [False, False]])>
The reduction version of this elementwise operation is tf.math.reduce_all
.
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 . |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/math/logical_and