def _subnetwork_generator(checkpoint_dir):
hparams = tf.contrib.training.HParams(
clip_gradients=5.,
stem_multiplier=3.0,
drop_path_keep_prob=0.6,
num_cells=3,
use_aux_head=False,
aux_head_weight=0.4,
label_smoothing=0.1,
num_conv_filters=4,
dense_dropout_keep_prob=1.0,
filter_scaling_rate=2.0,
complexity_decay_rate=0.9,
num_reduction_layers=2,
data_format="NHWC",
skip_reduction_layer_input=0,
initial_learning_rate=.01,
use_bounded_activation=False,
weight_decay=.0001,
knowledge_distillation=improve_nas.KnowledgeDistillation.NONE,
snapshot=False,
learn_mixture_weights=False,
mixture_weight_type=adanet.MixtureWeightType.SCALAR,
model_version="cifar",
total_training_steps=100)
return improve_nas.Generator(
[tf.feature_column.numeric_column(key="x", shape=[32, 32, 3])],
seed=11,
optimizer_fn=_optimizer,
iteration_steps=3,
checkpoint_dir=checkpoint_dir,
hparams=hparams)
def estimator(self,
data_provider,
run_config,
hparams,
train_steps=None,
seed=None):
"""Returns an AdaNet `Estimator` for train and evaluation.
Args:
data_provider: Data `Provider` for dataset to model.
run_config: `RunConfig` object to configure the runtime settings.
hparams: `HParams` instance defining custom hyperparameters.
train_steps: number of train steps.
seed: An integer seed if determinism is required.
Returns:
Returns an `Estimator`.
"""
max_iteration_steps = int(train_steps / hparams.boosting_iterations)
optimizer_fn = optimizer.fn_with_name(
hparams.optimizer,
learning_rate_schedule=hparams.learning_rate_schedule,
cosine_decay_steps=max_iteration_steps)
hparams.add_hparam("total_training_steps", max_iteration_steps)
if hparams.generator == GeneratorType.SIMPLE:
subnetwork_generator = improve_nas.Generator(
feature_columns=data_provider.get_feature_columns(),
optimizer_fn=optimizer_fn,
iteration_steps=max_iteration_steps,
checkpoint_dir=run_config.model_dir,
hparams=hparams,
seed=seed)
elif hparams.generator == GeneratorType.DYNAMIC:
subnetwork_generator = improve_nas.DynamicGenerator(
feature_columns=data_provider.get_feature_columns(),
optimizer_fn=optimizer_fn,
iteration_steps=max_iteration_steps,
checkpoint_dir=run_config.model_dir,
hparams=hparams,
seed=seed)
else:
raise ValueError("Invalid generator: `%s`" % hparams.generator)
evaluator = None
if hparams.use_evaluator:
evaluator = adanet.Evaluator(input_fn=data_provider.get_input_fn(
partition="train",
mode=tf.estimator.ModeKeys.EVAL,
batch_size=hparams.evaluator_batch_size),
steps=hparams.evaluator_steps)
return adanet.Estimator(
head=data_provider.get_head(),
subnetwork_generator=subnetwork_generator,
max_iteration_steps=max_iteration_steps,
adanet_lambda=hparams.adanet_lambda,
adanet_beta=hparams.adanet_beta,
mixture_weight_type=hparams.mixture_weight_type,
force_grow=hparams.force_grow,
evaluator=evaluator,
config=run_config,
model_dir=run_config.model_dir)