def build_subnetwork_spec(self, name, subnetwork_builder, summary, features, mode, labels=None, previous_ensemble=None, config=None, params=None): del summary del features del mode del labels del previous_ensemble del params del config return _SubnetworkSpec( name=name, subnetwork=None, builder=subnetwork_builder, step=tf.Variable(0.), predictions=None, loss=None, train_op=subnetwork_builder.build_subnetwork_train_op( *[None for _ in range(7)]), eval_metrics=tu.create_subnetwork_metrics( metric_fn=lambda: {"a": (tf.constant(1), tf.constant(1))}))
def build_subnetwork_spec(self, name, subnetwork_builder, summary, features, mode, labels=None, previous_ensemble=None, params=None): del summary del features del mode del labels del previous_ensemble del params is_training = False if subnetwork_builder: is_training = "training" in subnetwork_builder.name return _SubnetworkSpec( name=name, subnetwork=None, builder=subnetwork_builder, step=tf.Variable(0.), is_training=is_training, predictions=None, loss=None, train_op=subnetwork_builder.build_subnetwork_train_op( *[None for _ in range(7)]), eval_metrics=None)
def build_subnetwork_spec(self, name, subnetwork_builder, iteration_step, summary, features, mode, labels=None, previous_ensemble=None, params=None): del iteration_step del summary del features del mode del labels del previous_ensemble del params return _SubnetworkSpec( name=name, subnetwork=None, builder=subnetwork_builder, predictions=None, loss=None, train_op=subnetwork_builder.build_subnetwork_train_op( *[None for _ in range(7)]), eval_metrics=None)
def create_iteration_metrics(subnetwork_metrics=None, ensemble_metrics=None, use_tpu=False, iteration_number=1): """Creates an instance of the _IterationMetrics class. Args: subnetwork_metrics: List of _SubnetworkMetrics objects. ensemble_metrics: List of _EnsembleMetrics objects. use_tpu: Whether to use TPU-specific variable sharing logic. iteration_number: What number iteration these metrics are for. Returns: An instance of _IterationMetrics that has been populated with the input metrics. """ subnetwork_metrics = subnetwork_metrics or [] ensemble_metrics = ensemble_metrics or [] candidates = [] for i, metric in enumerate(ensemble_metrics): spec = _EnsembleSpec(name="ensemble_{}".format(i), ensemble=None, architecture=None, subnetwork_builders=None, predictions=None, step=None, variables=None, eval_metrics=metric) candidate = _Candidate(ensemble_spec=spec, adanet_loss=tf.constant(i), variables=None) candidates.append(candidate) subnetwork_specs = [] for i, metric in enumerate(subnetwork_metrics): spec = _SubnetworkSpec(name="subnetwork_{}".format(i), subnetwork=None, builder=None, predictions=None, step=None, loss=None, train_op=None, asset_dir=None, eval_metrics=metric, variables=None) subnetwork_specs.append(spec) return _IterationMetrics(iteration_number, candidates, subnetwork_specs=subnetwork_specs, use_tpu=use_tpu)