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:
bool
.tf.Tensor
of type bool
and one single bool
, where the result will be calculated by applying logical AND with the single element to each element in the larger Tensor.tf.Tensor
objects of type bool
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.
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