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)
