tf.control_dependencies

View source on GitHub

Wrapper for Graph.control_dependencies() using the default graph.

tf.control_dependencies(
    control_inputs
) -> Graph._ControlDependenciesController

Used in the notebooks

See tf.Graph.control_dependencies for more details.

In TensorFlow 2 with eager and/or Autograph, you should not need this method most of the times, as ops execute in the expected order thanks to automatic control dependencies. Only use it to manually control ordering, for example as a workaround to known issues such as tf.function with tf.debugging.assert* and tf.py_function. For example:

@tf.function(
  input_signature=[tf.TensorSpec([None, None], tf.float32),
                   tf.TensorSpec([None, None], tf.float32)])
def my_assert_func_1(x, bias):
  # `tf.function` attempts to execute `tf.math.add` in parallel to
  # `assert_equal`. As a result an error can get raised from `tf.math.add`
  # without triggering the assertion error.
  tf.assert_equal(tf.shape(x)[1],
                  tf.shape(bias)[1],
                  message='bad shape')
  return x + bias

# Error raised in either `add` or `assert`
my_assert_func_1(tf.ones((2, 5)), tf.ones((2, 7)))
Traceback (most recent call last):

InvalidArgumentError: ...

@tf.function(
  input_signature=[tf.TensorSpec([None, None], tf.float32),
                   tf.TensorSpec([None, None], tf.float32)])
def my_assert_func_2(x, bias):
  with tf.control_dependencies(
      [tf.assert_equal(tf.shape(x)[1],
                      tf.shape(bias)[1],
                      message='bad shape')]):
    return x + bias

# Error raised in `assert`
my_assert_func_2(tf.ones((2, 5)), tf.ones((2, 7)))
Traceback (most recent call last):

InvalidArgumentError: ...

When eager execution is enabled, any callable object in the control_inputs list will be called.