def create_experiment_summary():
"""Returns a summary proto buffer holding this experiment."""
# Convert TEMPERATURE_LIST to google.protobuf.ListValue
temperature_list = struct_pb2.ListValue()
temperature_list.extend(TEMPERATURE_LIST)
materials = struct_pb2.ListValue()
materials.extend(HEAT_COEFFICIENTS.keys())
return summary.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(name='initial_temperature',
display_name='Initial temperature',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=temperature_list),
api_pb2.HParamInfo(name='ambient_temperature',
display_name='Ambient temperature',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=temperature_list),
api_pb2.HParamInfo(name='material',
display_name='Material',
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=materials)
],
metric_infos=[
api_pb2.MetricInfo(name=api_pb2.MetricName(
tag='temperature/current/scalar_summary'),
display_name='Current Temp.'),
api_pb2.MetricInfo(name=api_pb2.MetricName(
tag='temperature/difference_to_ambient/scalar_summary'),
display_name='Difference To Ambient Temp.'),
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag='delta/scalar_summary'),
display_name='Delta T')
])
def create_experiment_summary():
"""Returns a summary proto buffer holding this experiment"""
# Convert TEMPERATURE_LIST to google.protobuf.ListValue
temperature_list = struct_pb2.ListValue()
temperature_list.extend(TEMPERATURE_LIST)
return summary.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(name="initial_temperature",
display_name="initial temperature",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=temperature_list),
api_pb2.HParamInfo(name="ambient_temperature",
display_name="ambient temperature",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=temperature_list),
api_pb2.HParamInfo(name="heat_coefficient",
display_name="heat coefficient",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=temperature_list)
],
metric_infos=[
api_pb2.MetricInfo(name=api_pb2.MetricName(
tag="temperature/current/scalar_summary"),
display_name="Current Temp."),
api_pb2.MetricInfo(name=api_pb2.MetricName(
tag="temperature/difference_to_ambient/scalar_summary"),
display_name="Difference To Ambient Temp."),
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag="delta/scalar_summary"),
display_name="Delta T")
])
def create_experiment_summary(optimizer_list, num_units_list, dropout_list):
optimizer_list_val = struct_pb2.ListValue()
optimizer_list_val.extend(optimizer_list)
num_units_list_val = struct_pb2.ListValue()
num_units_list_val.extend(num_units_list)
dropout_list_val = struct_pb2.ListValue()
dropout_list_val.extend(dropout_list)
return hparams_summary.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(
name='optimizer',
display_name='Optimizer',
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=optimizer_list_val),
api_pb2.HParamInfo(
name='num_units',
display_name='Number of units',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=num_units_list_val),
api_pb2.HParamInfo(
name='dropout',
display_name='Dropout',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=dropout_list_val),
],
metric_infos=[
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag='accuracy'), display_name='Accuracy'),
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag='elapse'), display_name='Elapse (s)')
])
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()),
)),
)
]),
)
def create_experiment_summary(num_units_list, dropout_rate_list, optimizer_list):
from tensorboard.plugins.hparams import api_pb2
from tensorboard.plugins.hparams import summary as hparams_summary
from google.protobuf import struct_pb2
num_units_list_val = struct_pb2.ListValue()
num_units_list_val.extend(num_units_list)
dropout_rate_list_val = struct_pb2.ListValue()
dropout_rate_list_val.extend(dropout_rate_list)
optimizer_list_val = struct_pb2.ListValue()
optimizer_list_val.extend(optimizer_list)
return hparams_summary.experiment_pb(
# The hyperparameters being changed
hparam_infos=[
api_pb2.HParamInfo(name='num_units',
display_name='Number of units',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=num_units_list_val),
api_pb2.HParamInfo(name='dropout_rate',
display_name='Dropout rate',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=dropout_rate_list_val),
api_pb2.HParamInfo(name='optimizer',
display_name='Optimizer',
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=optimizer_list_val)
],
# The metrics being tracked
metric_infos=[
api_pb2.MetricInfo(
name=api_pb2.MetricName(
tag='epoch_accuracy'),
display_name='Accuracy'),
]
)
def as_proto(self):
return api_pb2.MetricInfo(
name=api_pb2.MetricName(group=self._group, tag=self._tag,),
display_name=self._display_name,
description=self._description,
dataset_type=self._dataset_type,
)
def _to_summary_pb(num_units_list, dropout_rate_list, optimizer_list):
nus_val = struct_pb2.ListValue()
nus_val.extend(num_units_list)
drs_val = struct_pb2.ListValue()
drs_val.extend(dropout_rate_list)
opts_val = struct_pb2.ListValue()
opts_val.extend(optimizer_list)
return hparams.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(
name='num_units',
display_name='Number of units',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=nus_val),
api_pb2.HParamInfo(
name='dropout_rate',
display_name='Dropout rate',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=drs_val),
api_pb2.HParamInfo(
name='optimizer',
display_name='Optimizer',
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=opts_val)
],
metric_infos=[
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag='accuracy'),
display_name='Accuracy'),
])
def _to_summary_pb(num_units_list, dropout_rate_list, optimizer_list):
nus_val = struct_pb2.ListValue()
nus_val.extend(num_units_list)
drs_val = struct_pb2.ListValue()
drs_val.extend(dropout_rate_list)
opts_val = struct_pb2.ListValue()
opts_val.extend(optimizer_list)
return hparams.experiment_pb(
hparam_infos=[
api_pb2.HParamInfo(
name="num_units",
display_name="Number of units",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=nus_val,
),
api_pb2.HParamInfo(
name="drop_rate",
display_name="Dropout rate",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=drs_val,
),
api_pb2.HParamInfo(
name="optimizer",
display_name="Optimizer",
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=opts_val,
),
],
metric_infos=[
api_pb2.MetricInfo(name=api_pb2.MetricName(tag="accuracy"), display_name="Accuracy"),
],
)
def _compute_metric_infos(
self, experiment_id, hparams_run_to_tag_to_content
):
return (
api_pb2.MetricInfo(name=api_pb2.MetricName(group=group, tag=tag))
for tag, group in self._compute_metric_names(
experiment_id, hparams_run_to_tag_to_content
)
)
def _create_experiment_summary(self):
alpha_list_val = struct_pb2.ListValue()
alpha_list_val.extend(self._alpha_list)
alpha_decay_list_val = struct_pb2.ListValue()
alpha_decay_list_val.extend(self._alpha_decay_list)
gamma_list_val = struct_pb2.ListValue()
gamma_list_val.extend(self._gamma_list)
init_epsilon_list_val = struct_pb2.ListValue()
init_epsilon_list_val.extend(self._init_epsilon_list)
n_exploration_episodes_val = struct_pb2.ListValue()
n_exploration_episodes_val.extend(self._n_exploration_episodes)
return hparams_summary.experiment_pb(
# The hyperparameters being changed
hparam_infos=[
api_pb2.HParamInfo(name='alpha',
display_name='Learning rate',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=alpha_list_val),
api_pb2.HParamInfo(name='alpha_decay',
display_name='Learning rate decay',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=alpha_decay_list_val),
api_pb2.HParamInfo(name='gamma',
display_name='Reward discount factor',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=gamma_list_val),
api_pb2.HParamInfo(name='init_epsilon',
display_name='Initial exploration',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=init_epsilon_list_val),
api_pb2.HParamInfo(name='n_exploration_episodes',
display_name='Initial exploration',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=n_exploration_episodes_val)
],
# The metrics being tracked
metric_infos=[
api_pb2.MetricInfo(
name=api_pb2.MetricName(
tag='sum_reward'),
display_name='SumReward'),
]
)
def _create_experiment_summary(self):
alpha_list_val = struct_pb2.ListValue()
alpha_list_val.extend(self._alpha_list)
gamma_list_val = struct_pb2.ListValue()
gamma_list_val.extend(self._gamma_list)
return hparams_summary.experiment_pb(
# The hyperparameters being changed
hparam_infos=[
api_pb2.HParamInfo(name='alpha',
display_name='Learning rate',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=alpha_list_val),
api_pb2.HParamInfo(name='gamma',
display_name='Reward discount factor',
type=api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=gamma_list_val)
],
# The metrics being tracked
metric_infos=[
api_pb2.MetricInfo(
name=api_pb2.MetricName(tag='cummulative_reward'),
display_name='CumReward'),
])
def _compute_metric_infos(self):
return (api_pb2.MetricInfo(
name=api_pb2.MetricName(group=group, tag=tag))
for tag, group in self._compute_metric_names())
