def _model_fn(features, labels, mode, config): return model.model_builder( features=features, labels=labels, mode=mode, config=config, params={ 'head': core_quantile_regression_head( quantiles[0], label_dimension=label_dimension), 'feature_columns': feature_columns, 'learner_config': learner_config, 'num_trees': num_trees, 'weight_column_name': weight_column_name, 'examples_per_layer': examples_per_layer, 'center_bias': center_bias, 'logits_modifier_function': logits_modifier_function, 'use_core_libs': True, 'output_leaf_index': output_leaf_index, 'override_global_step_value': None, 'num_quantiles': num_quantiles, }, output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC)
def _model_fn(features, labels, mode, config): return model.model_builder( features=features, labels=labels, mode=mode, config=config, params={ 'head': head, 'feature_columns': feature_columns, 'learner_config': learner_config, 'num_trees': num_trees, 'weight_column_name': weight_column_name, 'examples_per_layer': examples_per_layer, 'center_bias': center_bias, 'logits_modifier_function': logits_modifier_function, 'use_core_libs': True, 'output_leaf_index': output_leaf_index, }, output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC)
def _model_fn(features, labels, mode, config): return model.model_builder( features=features, labels=labels, mode=mode, config=config, params={ 'head': head, 'feature_columns': feature_columns, 'learner_config': learner_config, 'num_trees': num_trees, 'weight_column_name': weight_column_name, 'examples_per_layer': examples_per_layer, 'center_bias': center_bias, 'logits_modifier_function': logits_modifier_function, 'use_core_libs': True, 'output_leaf_index': output_leaf_index, }, output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC)