def test_materialize_subnetwork_reports(self,
input_fn,
subnetwork_reports_fn,
steps,
iteration_number=0,
included_subnetwork_names=None,
want_materialized_reports=None):
with context.graph_mode():
tf.constant(0.) # dummy op so that the session graph is never empty.
features, labels = input_fn()
subnetwork_reports = subnetwork_reports_fn(features, labels)
with self.test_session() as sess:
sess.run(tf_compat.v1.initializers.local_variables())
report_materializer = ReportMaterializer(input_fn=input_fn, steps=steps)
materialized_reports = (
report_materializer.materialize_subnetwork_reports(
sess, iteration_number, subnetwork_reports,
included_subnetwork_names))
self.assertEqual(
len(want_materialized_reports), len(materialized_reports))
materialized_reports_dict = {
blrm.name: blrm for blrm in materialized_reports
}
for want_materialized_report in want_materialized_reports:
materialized_report = (
materialized_reports_dict[want_materialized_report.name])
self.assertEqual(iteration_number,
materialized_report.iteration_number)
self.assertEqual(
set(want_materialized_report.hparams.keys()),
set(materialized_report.hparams.keys()))
for hparam_key, want_hparam in (
want_materialized_report.hparams.items()):
if isinstance(want_hparam, float):
self.assertAllClose(want_hparam,
materialized_report.hparams[hparam_key])
else:
self.assertEqual(want_hparam,
materialized_report.hparams[hparam_key])
self.assertSetEqual(
set(want_materialized_report.attributes.keys()),
set(materialized_report.attributes.keys()))
for attribute_key, want_attribute in (
want_materialized_report.attributes.items()):
if isinstance(want_attribute, float):
self.assertAllClose(
want_attribute,
decode(materialized_report.attributes[attribute_key]))
else:
self.assertEqual(
want_attribute,
decode(materialized_report.attributes[attribute_key]))
self.assertSetEqual(
set(want_materialized_report.metrics.keys()),
set(materialized_report.metrics.keys()))
for metric_key, want_metric in (
want_materialized_report.metrics.items()):
if isinstance(want_metric, float):
self.assertAllClose(
want_metric, decode(materialized_report.metrics[metric_key]))
else:
self.assertEqual(want_metric,
decode(materialized_report.metrics[metric_key]))
def test_summaries(self):
"""Tests that summaries are written to candidate directory."""
run_config = tf.estimator.RunConfig(tf_random_seed=42,
log_step_count_steps=2,
save_summary_steps=2,
model_dir=self.test_subdirectory)
subnetwork_generator = SimpleGenerator([_SimpleBuilder("dnn")])
report_materializer = ReportMaterializer(input_fn=tu.dummy_input_fn(
[[1., 1.]], [[0.]]),
steps=1)
estimator = Estimator(head=regression_head.RegressionHead(
loss_reduction=tf_compat.SUM_OVER_BATCH_SIZE),
subnetwork_generator=subnetwork_generator,
report_materializer=report_materializer,
max_iteration_steps=10,
config=run_config)
train_input_fn = tu.dummy_input_fn([[1., 0.]], [[1.]])
estimator.train(input_fn=train_input_fn, max_steps=3)
ensemble_loss = 1.52950
self.assertAlmostEqual(ensemble_loss,
tu.check_eventfile_for_keyword(
"loss", self.test_subdirectory),
places=3)
self.assertIsNotNone(
tu.check_eventfile_for_keyword("global_step/sec",
self.test_subdirectory))
self.assertEqual(
0.,
tu.check_eventfile_for_keyword("iteration/adanet/iteration",
self.test_subdirectory))
subnetwork_subdir = os.path.join(self.test_subdirectory,
"subnetwork/t0_dnn")
self.assertAlmostEqual(3.,
tu.check_eventfile_for_keyword(
"scalar", subnetwork_subdir),
places=3)
self.assertEqual(
(3, 3, 1),
tu.check_eventfile_for_keyword("image", subnetwork_subdir))
self.assertAlmostEqual(5.,
tu.check_eventfile_for_keyword(
"nested/scalar", subnetwork_subdir),
places=3)
ensemble_subdir = os.path.join(
self.test_subdirectory,
"ensemble/t0_dnn_grow_complexity_regularized")
self.assertAlmostEqual(
ensemble_loss,
tu.check_eventfile_for_keyword(
"adanet_loss/adanet/adanet_weighted_ensemble",
ensemble_subdir),
places=1)
self.assertAlmostEqual(
0.,
tu.check_eventfile_for_keyword(
"complexity_regularization/adanet/adanet_weighted_ensemble",
ensemble_subdir),
places=3)
self.assertAlmostEqual(1.,
tu.check_eventfile_for_keyword(
"mixture_weight_norms/adanet/"
"adanet_weighted_ensemble/subnetwork_0",
ensemble_subdir),
places=3)