tf.tuple

Groups tensors together.

tf.tuple(
    tensors, control_inputs=None, name=None
)

The returned tensors have the same value as the input tensors, but they are computed only after all the input tensors have been computed.

Note: In TensorFlow 2 with eager and/or Autograph, you should not require this method, as ops execute in the expected order thanks to automatic control dependencies. Only use tf.tuple when working with v1 tf.Graph code.

See also tf.group and tf.control_dependencies.

Example:

with tf.Graph().as_default():
  with tf.compat.v1.Session() as sess:
    v = tf.Variable(0.0)
    a = tf.constant(1.0)
    sess.run(tf.compat.v1.global_variables_initializer())
    for i in range(5):
      update_op = v.assign_add(1.0)
      b = a + v
      res_b = sess.run(b)
      res_v = sess.run(v)
      print(res_v)
0.0
0.0
0.0
0.0
0.0

with tf.Graph().as_default():
  with tf.compat.v1.Session() as sess:
    v = tf.Variable(0.0)
    a = tf.constant(1.0)
    sess.run(tf.compat.v1.global_variables_initializer())
    for i in range(5):
      update_op = v.assign_add(1.0)
      calc = [a + v]
      # `tf.tuple` ensures `update_op` is run before `b`
      b = tf.tuple(calc, [tf.group(update_op)])
      res_b = sess.run(b)
      res_v = sess.run(v)
      print(res_v)
1.0
2.0
3.0
4.0
5.0

Args
tensors	A list of Tensors or IndexedSlices, some entries can be None.
control_inputs	List of additional ops to finish before returning.
name	(optional) A name to use as a name_scope for the operation.

Returns
Same as tensors.

Raises
ValueError	If tensors does not contain any Tensor or IndexedSlices.
TypeError	If control_inputs is not a list of Operation or Tensor objects.