tf.compat.v1.assert_none_equal

Assert the condition x != y holds element-wise.

tf.compat.v1.assert_none_equal(
    x, y, data=None, summarize=None, message=None, name=None
)

Migrate to TF2

tf.compat.v1.assert_none_equal is compatible with eager execution and tf.function. Please use tf.debugging.assert_none_equal instead when migrating to TF2. Apart from data, all arguments are supported with the same argument name.

If you want to ensure the assert statements run before the potentially-invalid computation, please use tf.control_dependencies, as tf.function auto-control dependencies are insufficient for assert statements.

Structural Mapping to Native TF2

Before:

tf.compat.v1.assert_none_equal(
  x=x, y=y, data=data, summarize=summarize,
  message=message, name=name)

After:

tf.debugging.assert_none_equal(
  x=x, y=y, message=message,
  summarize=summarize, name=name)

TF1 & TF2 Usage Example

TF1:

g = tf.Graph()
with g.as_default():
  a = tf.compat.v1.placeholder(tf.float32, [2])
  b = tf.compat.v1.placeholder(tf.float32, [2])
  result = tf.compat.v1.assert_none_equal(a, b,
    message='"a != b" does not hold for the given inputs')
  with tf.compat.v1.control_dependencies([result]):
    sum_node = a + b
sess = tf.compat.v1.Session(graph=g)
val = sess.run(sum_node, feed_dict={a: [1, 2], b:[2, 1]})

TF2:

a = tf.Variable([1, 2], dtype=tf.float32)
b = tf.Variable([2, 1], dtype=tf.float32)
assert_op = tf.debugging.assert_none_equal(a, b, message=
  '"a != b" does not hold for the given inputs')
# When working with tf.control_dependencies
with tf.control_dependencies([assert_op]):
  val = a + b

Description

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] != y[i]. If both x and y are empty, this is trivially satisfied.

When running in graph mode, you should add a dependency on this operation to ensure that it runs. Example of adding a dependency to an operation:

with tf.control_dependencies([tf.compat.v1.assert_none_equal(x, y)]):
  output = tf.reduce_sum(x)

x Numeric Tensor.
y Numeric Tensor, same dtype as and broadcastable to x.
data The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y.
summarize Print this many entries of each tensor.
message A string to prefix to the default message.
name A name for this operation (optional). Defaults to "assert_none_equal".

Op that raises InvalidArgumentError if x != y is False.

InvalidArgumentError if the check can be performed immediately and x != y is False. The check can be performed immediately during eager execution or if x and y are statically known.