def _get_beta_accumulators(self):
with ops.init_scope():
graph = ops.get_default_graph()
return (
self._get_non_slot_variable("beta1_power", graph=graph),
self._get_non_slot_variable("beta2_power", graph=graph),
)
def _get_beta_accumulators(self):
with ops.init_scope():
if context.executing_eagerly():
graph = None
else:
graph = ops.get_default_graph()
return (self._get_non_slot_variable("beta1_power", graph=graph),
self._get_non_slot_variable("beta2_power", graph=graph))
def test_graph_item_context_scope():
g1 = ops.Graph()
i1 = graph_item.GraphItem(graph=g1)
assert graph_item._default_graph_item is None
with i1.as_default() as item:
assert graph_item._default_graph_item == i1
assert item._graph == g1
assert ops.get_default_graph() == g1
setattr(item, 'new_attr', 'new_value')
assert graph_item._default_graph_item is None
assert getattr(i1, 'new_attr') == 'new_value'
def begin(self):
if self.summary_writer is None and self.output_dir:
self.summary_writer = SummaryWriterCache.get(self.output_dir)
graph = ops.get_default_graph()
self.fake_seq = graph.get_tensor_by_name("model/" + FAKE_PROTEINS +
":0")
self.labels = graph.get_tensor_by_name("model/" + LABELS + ":0")
self.d_score = graph.get_tensor_by_name("model/d_score:0")
self.global_step_tensor = training_util._get_or_create_global_step_read(
)
if self.global_step_tensor is None:
raise RuntimeError("Could not global step tensor")
if self.fake_seq is None:
raise RuntimeError("Could not get fake seq tensor")
def begin(self):
graph = ops.get_default_graph()
self.acid_embeddings = graph.get_tensor_by_name(
"model/" + self.variable_name_to_restore + ":0")
def _get_niter(self):
with ops.init_scope():
graph = ops.get_default_graph()
return self._get_non_slot_variable("niter", graph=graph)