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.initialize_all_variables().run()
self.assertEqual(9, v3.eval())
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.initialize_all_variables().run()
self.assertEqual(9, v3.eval())
def _ExitGrad(op, grad):
"""Gradients for an exit op are calculated using an Enter op."""
graph = ops.get_default_graph()
# pylint: disable=protected-access
op_ctxt = op._get_control_flow_context()
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
if op_ctxt.grad_state:
raise TypeError("Second-order gradient for while loops not supported.")
if isinstance(grad, ops.Tensor):
grad_ctxt.AddName(grad.name)
else:
if not isinstance(
grad,
(indexed_slices.IndexedSlices, sparse_tensor.SparseTensor)):
raise TypeError(
f"Type {type(grad)} not supported, must be either"
"`indexed_slices.IndexedSlices` or `SparseTensor`.")
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
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