Python tensorflow.python.ops.control_flow_ops._Enter() Examples

The following are 6 code examples of tensorflow.python.ops.control_flow_ops._Enter(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.control_flow_ops , or try the search function .
Example #1
Source File: control_flow_grad.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _ExitGrad(op, grad):
  """Gradients for an exit op are calculated using an Enter op."""
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if not grad_ctxt.back_prop:
    # The flag `back_prop` is set by users to suppress gradient
    # computation for this loop. If the attribute `back_prop` is false,
    # no gradient computation.
    return None

  # pylint: disable=protected-access
  if op._get_control_flow_context().grad_state:
    raise TypeError("Second-order gradient for while loops not supported.")
  # pylint: enable=protected-access

  if isinstance(grad, ops.Tensor):
    grad_ctxt.AddName(grad.name)
  else:
    if not isinstance(grad, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(grad))
    grad_ctxt.AddName(grad.values.name)
    grad_ctxt.AddName(grad.indices.name)
    dense_shape = grad.dense_shape
    if dense_shape is not None:
      grad_ctxt.AddName(dense_shape.name)
  grad_ctxt.Enter()
  # pylint: disable=protected-access
  result = control_flow_ops._Enter(
      grad, grad_ctxt.name, is_constant=False,
      parallel_iterations=grad_ctxt.parallel_iterations,
      name="b_exit")
  # pylint: enable=protected-access
  grad_ctxt.loop_enters.append(result)
  grad_ctxt.Exit()
  return result
Example #2
Source File: control_flow_grad.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _ExitGrad(op, grad):
  """Gradients for an exit op are calculated using an Enter op."""
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if not grad_ctxt.back_prop:
    # The flag `back_prop` is set by users to suppress gradient
    # computation for this loop. If the attribute `back_prop` is false,
    # no gradient computation.
    return None

  # pylint: disable=protected-access
  if op._get_control_flow_context().grad_state:
    raise TypeError("Second-order gradient for while loops not supported.")
  # pylint: enable=protected-access

  if isinstance(grad, ops.Tensor):
    grad_ctxt.AddName(grad.name)
  else:
    if not isinstance(grad, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(grad))
    grad_ctxt.AddName(grad.values.name)
    grad_ctxt.AddName(grad.indices.name)
    dense_shape = grad.dense_shape
    if dense_shape is not None:
      grad_ctxt.AddName(dense_shape.name)
  enter_fn = control_flow_ops._Enter  # pylint: disable=protected-access
  grad_ctxt.Enter()
  result = enter_fn(grad, grad_ctxt.name, is_constant=False,
                    parallel_iterations=grad_ctxt.parallel_iterations,
                    name="b_exit")
  grad_ctxt.Exit()
  return result
Example #3
Source File: control_flow_ops_py_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def testRefEnter(self):
    with self.test_session():
      v = tf.Variable(7)

      enter_v = control_flow_ops._Enter(v, "foo_1", is_constant=True)
      nine = tf.constant(9)
      enter_nine = control_flow_ops.enter(nine, "foo_1")
      op = tf.assign(enter_v, enter_nine)
      v2 = control_flow_ops.with_dependencies([op], enter_v)
      v3 = control_flow_ops.exit(v2)
      tf.global_variables_initializer().run()
      self.assertEqual(9, v3.eval())
Example #4
Source File: control_flow_grad.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _ExitGrad(op, grad):
  """Gradients for an exit op are calculated using an Enter op."""
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if not grad_ctxt.back_prop:
    # The flag `back_prop` is set by users to suppress gradient
    # computation for this loop. If the attribute `back_prop` is false,
    # no gradient computation.
    return None

  # pylint: disable=protected-access
  if op._get_control_flow_context().grad_state:
    raise TypeError("Second-order gradient for while loops not supported.")
  # pylint: enable=protected-access

  if isinstance(grad, ops.Tensor):
    grad_ctxt.AddName(grad.name)
  else:
    if not isinstance(grad, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(grad))
    grad_ctxt.AddName(grad.values.name)
    grad_ctxt.AddName(grad.indices.name)
    if isinstance(grad, ops.IndexedSlices):
      dense_shape = grad.dense_shape
    else:
      dense_shape = grad.shape
    if dense_shape is not None:
      grad_ctxt.AddName(dense_shape.name)
  enter_fn = control_flow_ops._Enter  # pylint: disable=protected-access
  grad_ctxt.Enter()
  result = enter_fn(grad, grad_ctxt.name, is_constant=False,
                    parallel_iterations=grad_ctxt.parallel_iterations,
                    name="b_exit")
  grad_ctxt.Exit()
  return result
Example #5
Source File: control_flow_grad.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _ExitGrad(op, grad):
  """Gradients for an exit op are calculated using an Enter op."""
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if not grad_ctxt.back_prop:
    # The flag `back_prop` is set by users to suppress gradient
    # computation for this loop. If the attribute `back_prop` is false,
    # no gradient computation.
    return None

  # pylint: disable=protected-access
  if op._get_control_flow_context().grad_state:
    raise TypeError("Second-order gradient for while loops not supported.")
  # pylint: enable=protected-access

  if isinstance(grad, ops.Tensor):
    grad_ctxt.AddName(grad.name)
  else:
    if not isinstance(grad, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(grad))
    grad_ctxt.AddName(grad.values.name)
    grad_ctxt.AddName(grad.indices.name)
    dense_shape = grad.dense_shape
    if dense_shape is not None:
      grad_ctxt.AddName(dense_shape.name)
  grad_ctxt.Enter()
  # pylint: disable=protected-access
  result = control_flow_ops._Enter(
      grad, grad_ctxt.name, is_constant=False,
      parallel_iterations=grad_ctxt.parallel_iterations,
      name="b_exit")
  # pylint: enable=protected-access
  grad_ctxt.loop_enters.append(result)
  grad_ctxt.Exit()
  return result
Example #6
Source File: control_flow_grad.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _ExitGrad(op, grad):
  """Gradients for an exit op are calculated using an Enter op."""
  graph = ops.get_default_graph()
  # pylint: disable=protected-access
  grad_ctxt = graph._get_control_flow_context()
  # pylint: enable=protected-access
  if not grad_ctxt.back_prop:
    # The flag `back_prop` is set by users to suppress gradient
    # computation for this loop. If the attribute `back_prop` is false,
    # no gradient computation.
    return None

  # pylint: disable=protected-access
  if op._get_control_flow_context().grad_state:
    raise TypeError("Second-order gradient for while loops not supported.")
  # pylint: enable=protected-access

  if isinstance(grad, ops.Tensor):
    grad_ctxt.AddName(grad.name)
  else:
    if not isinstance(grad, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(grad))
    grad_ctxt.AddName(grad.values.name)
    grad_ctxt.AddName(grad.indices.name)
    dense_shape = grad.dense_shape
    if dense_shape is not None:
      grad_ctxt.AddName(dense_shape.name)
  enter_fn = control_flow_ops._Enter  # pylint: disable=protected-access
  grad_ctxt.Enter()
  result = enter_fn(grad, grad_ctxt.name, is_constant=False,
                    parallel_iterations=grad_ctxt.parallel_iterations,
                    name="b_exit")
  grad_ctxt.Exit()
  return result