def testMisc(self):
ns = NS()
ns.w = 0
ns["x"] = 3
ns.x = 1
ns.y = NS(z=2)
self.assertEqual(list(ns.Keys()), [("w", ), ("x", ), ("y", "z")])
self.assertEqual(list(ns.Values()), [0, 1, 2])
self.assertEqual(list(ns.Items()), [(("w", ), 0), (("x", ), 1),
(("y", "z"), 2)])
self.assertEqual(ns.AsDict(),
OrderedDict([("w", 0), ("x", 1), ("y", NS(z=2))]))
ns.Update(ns.y)
self.assertEqual(list(ns), [("w", ), ("x", ), ("y", "z"), ("z", )])
self.assertEqual(list(ns.Keys()), [("w", ), ("x", ), ("y", "z"),
("z", )])
self.assertEqual(list(ns.Values()), [0, 1, 2, 2])
self.assertEqual(list(ns.Items()), [(("w", ), 0), (("x", ), 1),
(("y", "z"), 2), (("z", ), 2)])
self.assertEqual(
ns.AsDict(),
OrderedDict([("w", 0), ("x", 1), ("y", NS(z=2)), ("z", 2)]))
ns = NS(v=2, w=NS(x=1, y=[3, NS(z=0)]))
self.assertItemsEqual([("v", ), ("w", "x"), ("w", "y", 0),
("w", "y", 1, "z")], list(ns.Keys()))
def run(self,
session,
examples,
max_step_count=None,
hp=None,
aggregates=None):
aggregates = NS(aggregates or {})
for key in "loss error".split():
if key not in aggregates:
aggregates[key] = util.MeanAggregate()
tensors = self.tensors.Extract(*[key for key in aggregates.Keys()])
tensors.Update(self.tensors.Extract("final_state.model"))
state = NS(step=0, model=self.model.initial_state(hp.batch_size))
try:
for batch in util.batches(examples, hp.batch_size):
for segment in util.segments(batch,
hp.segment_length,
overlap=hp.chunk_size):
if max_step_count is not None and state.step >= max_step_count:
raise StopIteration()
x, = util.examples_as_arrays(segment)
feed_dict = {self.tensors.x: x.T}
feed_dict.update(self.model.feed_dict(state.model))
values = NS.FlatCall(
ft.partial(session.run, feed_dict=feed_dict), tensors)
for key in aggregates.Keys():
aggregates[key].add(values[key])
sys.stderr.write(".")
state.model = values.final_state.model
state.step += 1
except StopIteration:
pass
sys.stderr.write("\n")
values = NS(
(key, aggregate.value) for key, aggregate in aggregates.Items())
values.summaries = [
tf.Summary.Value(tag="%s_valid" % key, simple_value=values[key])
for key in "loss error".split()
]
print "### evaluation loss %6.5f error %6.5f" % (values.loss,
values.error)
if np.isnan(values.loss):
raise ValueError("loss has become NaN")
return values