def run(self, session, examples, max_step_count=None, hooks=None, hp=None):
tensors = self.tensors.Extract(
"loss error summaries global_step training_op learning_rate final_state.model"
)
state = NS(global_step=tf.train.global_step(session,
self.tensors.global_step),
model=self.model.initial_state(hp.batch_size))
while True:
for batch in util.batches(examples, hp.batch_size):
for segment in util.segments(batch,
self.segment_length,
overlap=LEFTOVER):
if max_step_count is not None and state.global_step >= max_step_count:
return
hooks.Get("step.before", util.noop)(state)
x, = util.examples_as_arrays(segment)
feed_dict = {self.tensors.x: x.T}
feed_dict.update(self.model.feed_dict(state.model))
values = tfutil.run(session, tensors, feed_dict=feed_dict)
state.model = values.final_state.model
state.global_step = values.global_step
hooks.Get("step.after", util.noop)(state, values)
print("step #%d loss %f error %f learning rate %e" %
(values.global_step, values.loss, values.error,
values.learning_rate))
if np.isnan(values.loss):
raise ValueError("loss has become NaN")
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
def test_segmented_batches(self):
length = np.random.randint(2, 100)
segment_length = np.random.randint(1, length)
example_count = np.random.randint(2, 100)
batch_size = np.random.randint(1, example_count)
feature_shapes = [
np.random.randint(1, 10, size=np.random.randint(1, 4))
for _ in range(np.random.randint(1, 4))
]
examples = [[
np.random.rand(length, *shape) for shape in feature_shapes
] for _ in range(example_count)]
for batch in util.batches(examples, batch_size, augment=False):
for segment in util.segments(examples, segment_length):
self.assertEqual(batch_size, len(batch))
for features in segment:
self.assertEqual(len(feature_shapes), len(features))
for feature, feature_shape in util.equizip(
features, feature_shapes):
self.assertLessEqual(len(feature), segment_length)
self.assertEqual(tuple(feature_shape),
feature.shape[1:])
def run(self, session, examples, max_step_count=None, hooks=None, hp=None):
state = NS(global_step=tf.train.global_step(session,
self.tensors.global_step),
model=self.model.initial_state(hp.batch_size))
while True:
for batch in util.batches(examples, hp.batch_size):
for segment in util.segments(
batch,
# the last chunk is not processed, so grab
# one more to ensure we backpropagate
# through at least one full model cycle.
# TODO(cotim): rename segment_length to
# backprop_length?
hp.segment_length + hp.chunk_size,
overlap=hp.chunk_size):
if max_step_count is not None and state.global_step >= max_step_count:
return
hooks.Get("step.before", util.noop)(state)
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),
self.tensors.Extract(
"loss error summaries global_step training_op learning_rate final_state.model"
))
state.model = values.final_state.model
state.global_step = values.global_step
hooks.Get("step.after", util.noop)(state, values)
print("step #%d loss %f error %f learning rate %e" %
(values.global_step, values.loss, values.error,
values.learning_rate))
if np.isnan(values.loss):
raise ValueError("loss has become NaN")