tf.math.logical_and

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

Used in the notebooks

Used in the tutorials

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 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.
  • Two 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.

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.