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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"""Demonstrates multiclass MNIST TF Boosted trees example.
This example demonstrates how to run experiments with TF Boosted Trees on
a binary dataset. We use digits 4 and 9 from the original MNIST dataset.
Example Usage:
python tensorflow/contrib/boosted_trees/examples/binary_mnist.py \
--output_dir="/tmp/binary_mnist" --depth=4 --learning_rate=0.3 \
--batch_size=10761 --examples_per_layer=10761 --eval_batch_size=1030 \
--num_eval_steps=1 --num_trees=10 --l2=1 --vmodule=training_ops=1
When training is done, accuracy on eval data is reported. Point tensorboard
to the directory for the run to see how the training progresses:
tensorboard --logdir=/tmp/binary_mnist
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import numpy as np
import tensorflow as tf
from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier
from tensorflow.contrib.boosted_trees.proto import learner_pb2
from tensorflow.contrib.learn import learn_runner
def get_input_fn(data,
batch_size,
capacity=10000,
min_after_dequeue=3000):
"""Input function over MNIST data."""
# Keep only 4 and 9 digits.
ids = np.where((data.labels == 4) | (data.labels == 9))
images = data.images[ids]
labels = data.labels[ids]
# Make digit 4 label 1, 9 is 0.
labels = labels == 4
def _input_fn():
"""Prepare features and labels."""
images_batch, labels_batch = tf.train.shuffle_batch(
tensors=[images,
labels.astype(np.int32)],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
enqueue_many=True,
num_threads=4)
features_map = {"images": images_batch}
return features_map, labels_batch
return _input_fn
# Main config - creates a TF Boosted Trees Estimator based on flags.
def _get_tfbt(output_dir):
"""Configures TF Boosted Trees estimator based on flags."""
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer
learner_config.constraints.max_tree_depth = FLAGS.depth
growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
# Create a TF Boosted trees estimator that can take in custom loss.
estimator = GradientBoostedDecisionTreeClassifier(
learner_config=learner_config,
examples_per_layer=FLAGS.examples_per_layer,
model_dir=output_dir,
num_trees=FLAGS.num_trees,
center_bias=False,
config=run_config)
return estimator
def _make_experiment_fn(output_dir):
"""Creates experiment for gradient boosted decision trees."""
data = tf.contrib.learn.datasets.mnist.load_mnist()
train_input_fn = get_input_fn(data.train, FLAGS.batch_size)
eval_input_fn = get_input_fn(data.validation, FLAGS.eval_batch_size)
return tf.contrib.learn.Experiment(
estimator=_get_tfbt(output_dir),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=None,
eval_steps=FLAGS.num_eval_steps,
eval_metrics=None)
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(
"--output_dir",
type=str,
required=True,
help="Choose the dir for the output.")
parser.add_argument(
"--batch_size",
type=int,
default=1000,
help="The batch size for reading data.")
parser.add_argument(
"--eval_batch_size",
type=int,
default=1000,
help="Size of the batch for eval.")
parser.add_argument(
"--num_eval_steps",
type=int,
default=1,
help="The number of steps to run evaluation for.")
# Flags for gradient boosted trees config.
parser.add_argument(
"--depth", type=int, default=4, help="Maximum depth of weak learners.")
parser.add_argument(
"--l2", type=float, default=1.0, help="l2 regularization per batch.")
parser.add_argument(
"--learning_rate",
type=float,
default=0.1,
help="Learning rate (shrinkage weight) with which each new tree is added."
)
parser.add_argument(
"--examples_per_layer",
type=int,
default=1000,
help="Number of examples to accumulate stats for per layer.")
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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