def _write_hparams_config(log_dir, searchspace):
HPARAMS = _create_hparams_config(searchspace)
METRICS = [
hp.Metric(
"epoch_acc",
group="validation",
display_name="accuracy (val.)",
),
hp.Metric(
"epoch_loss",
group="validation",
display_name="loss (val.)",
),
hp.Metric(
"epoch_acc",
group="train",
display_name="accuracy (train)",
),
hp.Metric(
"epoch_loss",
group="train",
display_name="loss (train)",
),
]
with tf.summary.create_file_writer(log_dir).as_default():
hp.hparams_config(hparams=HPARAMS, metrics=METRICS)
def _HParamExperiment(hparams, metrics):
from tensorboard.plugins.hparams import summary_v2 as hp
return hp.hparams_config_pb(
hparams=[_HParam(key, vals) for key, vals in hparams.items()],
metrics=[hp.Metric(tag) for tag in metrics],
)
def setUp(self):
self.logdir = os.path.join(self.get_temp_dir(), "logs")
self.hparams = [
hp.HParam("learning_rate", hp.RealInterval(1e-2, 1e-1)),
hp.HParam("dense_layers", hp.IntInterval(2, 7)),
hp.HParam("optimizer", hp.Discrete(["adam", "sgd"])),
hp.HParam("who_knows_what"),
hp.HParam(
"magic",
hp.Discrete([False, True]),
display_name="~*~ Magic ~*~",
description="descriptive",
),
]
self.metrics = [
hp.Metric("samples_per_second"),
hp.Metric(group="train",
tag="batch_loss",
display_name="loss (train)"),
hp.Metric(
group="validation",
tag="epoch_accuracy",
display_name="accuracy (val.)",
description="Accuracy on the _validation_ dataset.",
dataset_type=hp.Metric.VALIDATION,
),
]
self.time_created_secs = 1555624767.0
self.expected_experiment_pb = api_pb2.Experiment()
text_format.Merge(
"""
time_created_secs: 1555624767.0
hparam_infos {
name: "learning_rate"
type: DATA_TYPE_FLOAT64
domain_interval {
min_value: 0.01
max_value: 0.1
}
}
hparam_infos {
name: "dense_layers"
type: DATA_TYPE_FLOAT64
domain_interval {
min_value: 2
max_value: 7
}
}
hparam_infos {
name: "optimizer"
type: DATA_TYPE_STRING
domain_discrete {
values {
string_value: "adam"
}
values {
string_value: "sgd"
}
}
}
hparam_infos {
name: "who_knows_what"
}
hparam_infos {
name: "magic"
type: DATA_TYPE_BOOL
display_name: "~*~ Magic ~*~"
description: "descriptive"
domain_discrete {
values {
bool_value: false
}
values {
bool_value: true
}
}
}
metric_infos {
name {
tag: "samples_per_second"
}
}
metric_infos {
name {
group: "train"
tag: "batch_loss"
}
display_name: "loss (train)"
}
metric_infos {
name {
group: "validation"
tag: "epoch_accuracy"
}
display_name: "accuracy (val.)"
description: "Accuracy on the _validation_ dataset."
dataset_type: DATASET_VALIDATION
}
""",
self.expected_experiment_pb,
)
def Metric(tag):
return hp.Metric(tag)
