def test_hparams_smoke(self):
hp = {'lr': 0.1, 'bsize': 4}
mt = {'accuracy': 0.1, 'loss': 10}
summary.hparams(hp, mt)
hp = {'string': "1b", 'use magic': True}
summary.hparams(hp, mt)
def _try_log_hparams(self, trial: "Trial", result: Dict):
# TBX currently errors if the hparams value is None.
flat_params = flatten_dict(trial.evaluated_params)
scrubbed_params = {
k: v
for k, v in flat_params.items()
if isinstance(v, self.VALID_HPARAMS)
}
removed = {
k: v
for k, v in flat_params.items()
if not isinstance(v, self.VALID_HPARAMS)
}
if removed:
logger.info(
"Removed the following hyperparameter values when "
"logging to tensorboard: %s", str(removed))
from tensorboardX.summary import hparams
try:
experiment_tag, session_start_tag, session_end_tag = hparams(
hparam_dict=scrubbed_params, metric_dict=result)
self._trial_writer[trial].file_writer.add_summary(experiment_tag)
self._trial_writer[trial].file_writer.add_summary(
session_start_tag)
self._trial_writer[trial].file_writer.add_summary(session_end_tag)
except Exception:
logger.exception("TensorboardX failed to log hparams. "
"This may be due to an unsupported type "
"in the hyperparameter values.")
def close(self):
if self._file_writer is not None:
if self.trial and self.trial.evaluated_params and self.last_result:
from tensorboardX.summary import hparams
experiment_tag, session_start_tag, session_end_tag = hparams(
hparam_dict=self.trial.evaluated_params,
metric_dict=self.last_result)
self._file_writer.file_writer.add_summary(experiment_tag)
self._file_writer.file_writer.add_summary(session_start_tag)
self._file_writer.file_writer.add_summary(session_end_tag)
self._file_writer.close()
def log_args(self, args, tag=None):
if hparams is not None:
writer = self._writer(tag or '')
hp, met = self._args2hparams(args)
exp, ssi, sei = hparams(hp, met)
writer.file_writer.add_summary(exp)
writer.file_writer.add_summary(ssi)
writer.file_writer.add_summary(sei)
writer.flush()
def _try_log_hparams(self, result):
# TBX currently errors if the hparams value is None.
scrubbed_params = {
k: v
for k, v in self.trial.evaluated_params.items() if v is not None
}
from tensorboardX.summary import hparams
experiment_tag, session_start_tag, session_end_tag = hparams(
hparam_dict=scrubbed_params, metric_dict=result)
self._file_writer.file_writer.add_summary(experiment_tag)
self._file_writer.file_writer.add_summary(session_start_tag)
self._file_writer.file_writer.add_summary(session_end_tag)
def add_hparams(self,
hparam_dict=None,
metric_dict=None,
name=None,
global_step=None):
if type(hparam_dict) is not dict or type(metric_dict) is not dict:
raise TypeError(
'hparam_dict and metric_dict should be dictionary.')
exp, ssi, sei = hparams(hparam_dict, metric_dict)
self.file_writer.add_summary(exp)
self.file_writer.add_summary(ssi)
self.file_writer.add_summary(sei)
for k, v in metric_dict.items():
self.add_scalar(k, v, global_step)
def _write_hparams(self, hparam_dict, metric_dict, name):
# adapted from
# https://github.com/lanpa/tensorboardX/blob/master/tensorboardX/writer.py#L336-L376
exp, ssi, sei = hparams(hparam_dict, metric_dict)
w_hp = SummaryWriter(
logdir=os.path.join(self._file_writer.logdir, name))
w_hp.file_writer.add_summary(exp)
w_hp.file_writer.add_summary(ssi)
w_hp.file_writer.add_summary(sei)
for k, values in metric_dict.items():
global_step = 0
for v in values:
w_hp.add_scalar(k, v, global_step)
global_step += 1
w_hp.close()
def test_hparams(self):
hp = {'lr': 0.1}
mt = {'accuracy': 0.1}
compare_proto(summary.hparams(hp, mt), self)
def test_hparams_string(self):
hp = {'string_var': "hi"}
mt = {'accuracy': 0.1}
compare_proto(summary.hparams(hp, mt), self)
def test_hparams_bool(self):
hp = {'bool_var': True}
mt = {'accuracy': 0.1}
compare_proto(summary.hparams(hp, mt), self)
def test_hparams_smoke(self):
hp = {'lr': 0.1, 'bsize': 4}
mt = {'accuracy': 0.1, 'loss': 10}
summary.hparams(hp, mt)
