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Prepare variance to be exported for serving with the servo library.

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PiperOrigin-RevId: 183851026
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A. Unique TensorFlower 2018-01-30 10:18:36 -08:00 committed by TensorFlower Gardener
parent 7149a2e2e2
commit 8f0e720777
1 changed files with 34 additions and 7 deletions
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41
tensorflow/contrib/tensor_forest/client/random_forest.py
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@ -18,7 +18,7 @@ from __future__ import division
from __future__ import print_function
from tensorflow.contrib import layers
from tensorflow.contrib.learn.python.learn.estimators import constants
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
@ -43,8 +43,8 @@ from tensorflow.python.training import training_util
KEYS_NAME = 'keys'
LOSS_NAME = 'rf_training_loss'
TREE_PATHS_PREDICTION_KEY = 'tree_paths'
VARIANCE_PREDICTION_KEY = 'regression_variance'
VARIANCE_PREDICTION_KEY = 'prediction_variance'
ALL_SERVING_KEY = 'tensorforest_all'
EPSILON = 0.000001
@ -134,7 +134,8 @@ def get_model_fn(params,
trainer_id=0,
report_feature_importances=False,
local_eval=False,
head_scope=None):
head_scope=None,
include_all_in_serving=False):
"""Return a model function given a way to construct a graph builder."""
if model_head is None:
model_head = get_default_head(params, weights_name)
@ -238,7 +239,13 @@ def get_model_fn(params,
model_ops.predictions[TREE_PATHS_PREDICTION_KEY] = tree_paths
model_ops.predictions[VARIANCE_PREDICTION_KEY] = regression_variance
if include_all_in_serving:
# In order to serve the variance we need to add the prediction dict
# to output_alternatives dict.
if not model_ops.output_alternatives:
model_ops.output_alternatives = {}
model_ops.output_alternatives[ALL_SERVING_KEY] = (
constants.ProblemType.UNSPECIFIED, model_ops.predictions)
return model_ops
return _model_fn
@ -293,7 +300,8 @@ class TensorForestEstimator(estimator.Estimator):
report_feature_importances=False,
local_eval=False,
version=None,
head=None):
head=None,
include_all_in_serving=False):
"""Initializes a TensorForestEstimator instance.
Args:
@ -339,6 +347,23 @@ class TensorForestEstimator(estimator.Estimator):
version: Unused.
head: A heads_lib.Head object that calculates losses and such. If None,
one will be automatically created based on params.
include_all_in_serving: if True, allow preparation of the complete
prediction dict including the variance to be exported for serving with
the Servo lib; and it also requires calling export_savedmodel with
default_output_alternative_key=ALL_SERVING_KEY, i.e.
estimator.export_savedmodel(export_dir_base=your_export_dir,
serving_input_fn=your_export_input_fn,
default_output_alternative_key=ALL_SERVING_KEY)
if False, resort to default behavior, i.e. export scores and
probabilities but no variances. In this case
default_output_alternative_key should be None while calling
export_savedmodel().
Note, that due to backward compatibility we cannot always set
include_all_in_serving to True because in this case calling
export_saved_model() without
default_output_alternative_key=ALL_SERVING_KEY (legacy behavior) the
saved_model_export_utils.get_output_alternatives() would raise
ValueError.
Returns:
A `TensorForestEstimator` instance.
@ -357,7 +382,9 @@ class TensorForestEstimator(estimator.Estimator):
num_trainers=num_trainers,
trainer_id=trainer_id,
report_feature_importances=report_feature_importances,
local_eval=local_eval),
local_eval=local_eval,
include_all_in_serving=include_all_in_serving,
),
model_dir=model_dir,
config=config,
feature_engineering_fn=feature_engineering_fn)