def experiment_fn(run_config, hparams):
estimator = tf.estimator.Estimator(model_fn=make_model_fn(),
config=run_config,
params=hparams)
train_hooks = [
hooks.ExamplesPerSecondHook(batch_size=hparams.batch_size,
every_n_iter=FLAGS.save_summary_steps),
hooks.LoggingTensorHook(collection="batch_logging",
every_n_iter=FLAGS.save_summary_steps,
batch=True),
hooks.LoggingTensorHook(collection="logging",
every_n_iter=FLAGS.save_summary_steps,
batch=False)
]
eval_hooks = [
hooks.SummarySaverHook(every_n_iter=FLAGS.save_summary_steps,
output_dir=os.path.join(run_config.model_dir,
"eval"))
]
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=make_input_fn(tf.estimator.ModeKeys.TRAIN, hparams),
eval_input_fn=make_input_fn(tf.estimator.ModeKeys.EVAL, hparams),
eval_steps=None,
min_eval_frequency=FLAGS.eval_frequency,
eval_hooks=eval_hooks)
experiment.extend_train_hooks(train_hooks)
return experiment
def experiment_fn(run_config, hparams):
"""Constructs an experiment object."""
estimator = learn.Estimator(
model_fn=make_model_fn(), config=run_config, params=hparams)
train_hooks = [
hooks.ExamplesPerSecondHook(hparams.batch_size, FLAGS.save_summary_steps)]
experiment = learn.Experiment(
estimator=estimator,
train_input_fn=make_input_fn(learn.ModeKeys.TRAIN, hparams),
eval_input_fn=make_input_fn(learn.ModeKeys.EVAL, hparams),
eval_metrics=MODELS[FLAGS.model].eval_metrics(hparams),
eval_steps=FLAGS.eval_steps,
min_eval_frequency=FLAGS.eval_frequency)
experiment.extend_train_hooks(train_hooks)
return experiment
def _train():
params_path = os.path.join(model_dir, "params.json")
if os.path.exists(params_path) and not FLAGS.overwrite_params:
with open(params_path, "r") as fp:
if not fp.read() == str(hp):
raise RuntimeError("Mismatching parameters found.")
else:
with open(params_path, "w") as fp:
fp.write(str(hp))
train_sets = (
cfg.train_sets.to_dict()
if isinstance(cfg.train_sets, misc_utils.Tuple)
else cfg.train_sets)
estimator.train(
input_fn=io_utils.make_input_fn(
d, train_sets,
tf.estimator.ModeKeys.TRAIN, hp,
num_epochs=cfg.num_epochs,
shuffle_batches=cfg.shuffle_batches,
num_threads=cfg.num_reader_threads,
prefetch_buffer_size=cfg.prefetch_buffer_size),
hooks=[
hooks.ExamplesPerSecondHook(
batch_size=hp.batch_size,
every_n_iter=cfg.save_summary_steps),
hooks.LoggingTensorHook(
collection="batch_logging",
every_n_iter=cfg.save_summary_steps,
batch=True),
hooks.LoggingTensorHook(
collection="logging",
every_n_iter=cfg.save_summary_steps,
batch=False),
tf.train.CheckpointSaverHook(
model_dir,
save_steps=cfg.save_checkpoints_steps,
listeners=[
hooks.BestCheckpointKeeper(
model_dir,
eval_fn=_eval,
eval_set=cfg.checkpoint_selector.eval_set,
eval_metric=cfg.checkpoint_selector.eval_metric,
compare_fn=cfg.checkpoint_selector.compare_fn)])])
def experiment_fn(run_config, hparams):
estimator = tf.estimator.Estimator(model_fn=optimizer.make_model_fn(
MODELS[FLAGS.model].model, FLAGS.num_gpus),
config=run_config,
params=hparams)
train_hooks = [
hooks.ExamplesPerSecondHook(batch_size=hparams.batch_size,
every_n_iter=FLAGS.save_summary_steps),
hooks.LoggingTensorHook(collection="batch_logging",
every_n_iter=FLAGS.save_summary_steps,
batch=True),
hooks.LoggingTensorHook(collection="logging",
every_n_iter=FLAGS.save_summary_steps,
batch=False)
]
eval_hooks = [
hooks.SummarySaverHook(every_n_iter=FLAGS.save_summary_steps,
output_dir=os.path.join(run_config.model_dir,
"eval"))
]
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=common_io.make_input_fn(
DATASETS[FLAGS.dataset],
tf.estimator.ModeKeys.TRAIN,
hparams,
num_epochs=FLAGS.num_epochs,
shuffle_batches=FLAGS.shuffle_batches,
num_threads=FLAGS.num_reader_threads),
eval_input_fn=common_io.make_input_fn(
DATASETS[FLAGS.dataset],
tf.estimator.ModeKeys.EVAL,
hparams,
num_epochs=FLAGS.num_epochs,
shuffle_batches=FLAGS.shuffle_batches,
num_threads=FLAGS.num_reader_threads),
eval_steps=None,
min_eval_frequency=FLAGS.eval_frequency,
eval_hooks=eval_hooks)
experiment.extend_train_hooks(train_hooks)
return experiment