def _get_train_op_and_ensemble(self, head, config, is_classification, train_in_memory): """Calls bt_model_fn() and returns the train_op and ensemble_serialzed.""" features, labels = _make_train_input_fn(is_classification)() estimator_spec = boosted_trees._bt_model_fn( # pylint:disable=protected-access features=features, labels=labels, mode=model_fn.ModeKeys.TRAIN, head=head, feature_columns=self._feature_columns, tree_hparams=self._tree_hparams, example_id_column_name=EXAMPLE_ID_COLUMN, n_batches_per_layer=1, config=config, train_in_memory=train_in_memory) resources.initialize_resources(resources.shared_resources()).run() variables.global_variables_initializer().run() variables.local_variables_initializer().run() # Gets the train_op and serialized proto of the ensemble. shared_resources = resources.shared_resources() self.assertEqual(1, len(shared_resources)) train_op = estimator_spec.train_op with ops.control_dependencies([train_op]): _, ensemble_serialized = ( gen_boosted_trees_ops.boosted_trees_serialize_ensemble( shared_resources[0].handle)) return train_op, ensemble_serialized
def _get_train_op_and_ensemble(self, head, config, is_classification, train_in_memory): """Calls bt_model_fn() and returns the train_op and ensemble_serialzed.""" features, labels = _make_train_input_fn(is_classification)() estimator_spec = boosted_trees._bt_model_fn( # pylint:disable=protected-access features=features, labels=labels, mode=model_fn.ModeKeys.TRAIN, head=head, feature_columns=self._feature_columns, tree_hparams=self._tree_hparams, example_id_column_name=EXAMPLE_ID_COLUMN, n_batches_per_layer=1, config=config, train_in_memory=train_in_memory) resources.initialize_resources(resources.shared_resources()).run() variables.global_variables_initializer().run() variables.local_variables_initializer().run() # Gets the train_op and serialized proto of the ensemble. shared_resources = resources.shared_resources() self.assertEqual(1, len(shared_resources)) train_op = estimator_spec.train_op with ops.control_dependencies([train_op]): _, ensemble_serialized = ( gen_boosted_trees_ops.boosted_trees_serialize_ensemble( shared_resources[0].handle)) return train_op, ensemble_serialized
def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn(features, labels, mode, head, feature_columns, tree_hparams, n_batches_per_layer, config=config)
def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( features, labels, mode, head, feature_columns, tree_hparams, n_batches_per_layer, config=config)
def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( features, labels, mode, head, feature_columns, tree_hparams, n_batches_per_layer=1, config=config, closed_form_grad_and_hess_fn=closed_form, train_in_memory=True)
def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( features, labels, mode, head, feature_columns, tree_hparams, n_batches_per_layer=1, config=config, closed_form_grad_and_hess_fn=closed_form, train_in_memory=True)
def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( # pylint: disable=protected-access features, labels, mode, head, feature_columns, tree_hparams, n_batches_per_layer, config)