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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Regression on Boston housing data using DNNBoostedTreeCombinedRegressor.
Example Usage:
python tensorflow/contrib/boosted_trees/examples/boston_combined.py \
--batch_size=404 --output_dir="/tmp/boston" \
--dnn_hidden_units="8,4" --dnn_steps_to_train=1000 \
--tree_depth=4 --tree_learning_rate=0.1 \
--num_trees=100 --tree_l2=0.001 --num_eval_steps=1 \
--vmodule=training_ops=1
When training is done, mean squared error on eval data is reported.
Point tensorboard to the directory for the run to see how the training
progresses:
tensorboard --logdir=/tmp/boston
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
from tensorflow.contrib.boosted_trees.estimator_batch.dnn_tree_combined_estimator import DNNBoostedTreeCombinedRegressor
from tensorflow.contrib.boosted_trees.proto import learner_pb2
from tensorflow.contrib.layers.python.layers import feature_column
from tensorflow.contrib.learn.python.learn import learn_runner
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
_BOSTON_NUM_FEATURES = 13
def _get_estimator(output_dir, feature_cols):
"""Configures DNNBoostedTreeCombinedRegressor based on flags."""
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = (
FLAGS.tree_learning_rate)
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = FLAGS.tree_l2
learner_config.constraints.max_tree_depth = FLAGS.tree_depth
run_config = tf.contrib.learn.RunConfig(save_summary_steps=1)
# Create a DNNBoostedTreeCombinedRegressor estimator.
estimator = DNNBoostedTreeCombinedRegressor(
dnn_hidden_units=[int(x) for x in FLAGS.dnn_hidden_units.split(",")],
dnn_feature_columns=feature_cols,
tree_learner_config=learner_config,
num_trees=FLAGS.num_trees,
# This should be the number of examples. For large datasets it can be
# larger than the batch_size.
tree_examples_per_layer=FLAGS.batch_size,
model_dir=output_dir,
config=run_config,
dnn_input_layer_to_tree=True,
dnn_steps_to_train=FLAGS.dnn_steps_to_train)
return estimator
def _make_experiment_fn(output_dir):
"""Creates experiment for DNNBoostedTreeCombinedRegressor."""
(x_train, y_train), (x_test,
y_test) = tf.keras.datasets.boston_housing.load_data()
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": x_train},
y=y_train,
batch_size=FLAGS.batch_size,
num_epochs=None,
shuffle=True)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": x_test}, y=y_test, num_epochs=1, shuffle=False)
feature_columns = [
feature_column.real_valued_column("x", dimension=_BOSTON_NUM_FEATURES)
]
feature_spec = tf.contrib.layers.create_feature_spec_for_parsing(
feature_columns)
serving_input_fn = input_fn_utils.build_parsing_serving_input_fn(feature_spec)
export_strategies = [
saved_model_export_utils.make_export_strategy(serving_input_fn)]
return tf.contrib.learn.Experiment(
estimator=_get_estimator(output_dir, feature_columns),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=None,
eval_steps=FLAGS.num_eval_steps,
eval_metrics=None,
export_strategies=export_strategies)
def main(unused_argv):
learn_runner.run(
experiment_fn=_make_experiment_fn,
output_dir=FLAGS.output_dir,
schedule="train_and_evaluate")
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
parser = argparse.ArgumentParser()
# Define the list of flags that users can change.
parser.add_argument(
"--batch_size",
type=int,
default=1000,
help="The batch size for reading data.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Choose the dir for the output.")
parser.add_argument(
"--num_eval_steps",
type=int,
default=1,
help="The number of steps to run evaluation for.")
# Flags for configuring DNNBoostedTreeCombinedRegressor.
parser.add_argument(
"--dnn_hidden_units",
type=str,
default="8,4",
help="Hidden layers for DNN.")
parser.add_argument(
"--dnn_steps_to_train",
type=int,
default=1000,
help="Number of steps to train DNN.")
parser.add_argument(
"--tree_depth", type=int, default=4, help="Maximum depth of trees.")
parser.add_argument(
"--tree_l2", type=float, default=1.0, help="l2 regularization per batch.")
parser.add_argument(
"--tree_learning_rate",
type=float,
default=0.1,
help=("Learning rate (shrinkage weight) with which each "
"new tree is added."))
parser.add_argument(
"--num_trees",
type=int,
default=None,
required=True,
help="Number of trees to grow before stopping.")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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