def create_experiment_summary():
"""Create an `api_pb2.Experiment` proto describing the experiment."""
def discrete_domain(values):
domain = struct_pb2.ListValue()
domain.extend(values)
return domain
hparams = [
api_pb2.HParamInfo(
name="conv_layers",
type=api_pb2.DATA_TYPE_FLOAT64, # actually int
domain_discrete=discrete_domain([1, 2, 3]),
),
api_pb2.HParamInfo(
name="conv_kernel_size",
type=api_pb2.DATA_TYPE_FLOAT64, # actually int
domain_discrete=discrete_domain([3, 5]),
),
api_pb2.HParamInfo(
name="dense_layers",
type=api_pb2.DATA_TYPE_FLOAT64, # actually int
domain_discrete=discrete_domain([1, 2, 3]),
),
api_pb2.HParamInfo(
name="dropout",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_interval=api_pb2.Interval(min_value=0.1, max_value=0.4),
),
api_pb2.HParamInfo(
name="optimizer",
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=discrete_domain(["adam", "adagrad"]),
),
]
metrics = [
api_pb2.MetricInfo(
name=api_pb2.MetricName(group="validation", tag="epoch_accuracy"),
display_name="accuracy (val.)",
),
api_pb2.MetricInfo(
name=api_pb2.MetricName(group="validation", tag="epoch_loss"),
display_name="loss (val.)",
),
api_pb2.MetricInfo(
name=api_pb2.MetricName(group="train", tag="batch_accuracy"),
display_name="accuracy (train)",
),
api_pb2.MetricInfo(
name=api_pb2.MetricName(group="train", tag="batch_loss"),
display_name="loss (train)",
),
]
return hparams_summary.experiment_pb(
hparam_infos=hparams,
metric_infos=metrics,
)
def test_experiment_pb(self):
hparam_infos = [
api_pb2.HParamInfo(
name="param1",
display_name="display_name1",
description="foo",
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=struct_pb2.ListValue(values=[
struct_pb2.Value(string_value="a"),
struct_pb2.Value(string_value="b"),
]),
),
api_pb2.HParamInfo(
name="param2",
display_name="display_name2",
description="bar",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_interval=api_pb2.Interval(min_value=-100.0,
max_value=100.0),
),
]
metric_infos = [
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag="loss"),
dataset_type=api_pb2.DATASET_VALIDATION,
),
api_pb2.MetricInfo(
name=api_pb2.MetricName(group="train/", tag="acc"),
dataset_type=api_pb2.DATASET_TRAINING,
),
]
time_created_secs = 314159.0
self.assertEqual(
summary.experiment_pb(hparam_infos,
metric_infos,
time_created_secs=time_created_secs),
tf.compat.v1.Summary(value=[
tf.compat.v1.Summary.Value(
tag="_hparams_/experiment",
tensor=summary._TF_NULL_TENSOR,
metadata=tf.compat.v1.SummaryMetadata(
plugin_data=tf.compat.v1.SummaryMetadata.PluginData(
plugin_name="hparams",
content=(plugin_data_pb2.HParamsPluginData(
version=0,
experiment=api_pb2.Experiment(
time_created_secs=time_created_secs,
hparam_infos=hparam_infos,
metric_infos=metric_infos,
),
).SerializeToString()),
)),
)
]),
)