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# Copyright 2016 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.
# ==============================================================================
"""A stand-alone example for tf.learn's random forest model on mnist."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tempfile
import numpy
from tensorflow.contrib.learn.python.learn import metric_spec
from tensorflow.contrib.tensor_forest.client import eval_metrics
from tensorflow.contrib.tensor_forest.client import random_forest
from tensorflow.contrib.tensor_forest.python import tensor_forest
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.estimator.inputs import numpy_io
from tensorflow.python.platform import app
FLAGS = None
def build_estimator(model_dir):
"""Build an estimator."""
params = tensor_forest.ForestHParams(
num_classes=10,
num_features=784,
num_trees=FLAGS.num_trees,
max_nodes=FLAGS.max_nodes)
graph_builder_class = tensor_forest.RandomForestGraphs
if FLAGS.use_training_loss:
graph_builder_class = tensor_forest.TrainingLossForest
return random_forest.TensorForestEstimator(
params, graph_builder_class=graph_builder_class, model_dir=model_dir)
def train_and_eval():
"""Train and evaluate the model."""
model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
print('model directory = %s' % model_dir)
est = build_estimator(model_dir)
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=False)
train_input_fn = numpy_io.numpy_input_fn(
x={'images': mnist.train.images},
y=mnist.train.labels.astype(numpy.int32),
batch_size=FLAGS.batch_size,
num_epochs=None,
shuffle=True)
est.fit(input_fn=train_input_fn, steps=None)
metric_name = 'accuracy'
metric = {
metric_name:
metric_spec.MetricSpec(
eval_metrics.get_metric(metric_name),
prediction_key=eval_metrics.get_prediction_key(metric_name))
}
test_input_fn = numpy_io.numpy_input_fn(
x={'images': mnist.test.images},
y=mnist.test.labels.astype(numpy.int32),
num_epochs=1,
batch_size=FLAGS.batch_size,
shuffle=False)
results = est.evaluate(input_fn=test_input_fn, metrics=metric)
for key in sorted(results):
print('%s: %s' % (key, results[key]))
def main(_):
train_and_eval()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_dir',
type=str,
default='',
help='Base directory for output models.'
)
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/data/',
help='Directory for storing data'
)
parser.add_argument(
'--train_steps',
type=int,
default=1000,
help='Number of training steps.'
)
parser.add_argument(
'--batch_size',
type=str,
default=1000,
help='Number of examples in a training batch.'
)
parser.add_argument(
'--num_trees',
type=int,
default=100,
help='Number of trees in the forest.'
)
parser.add_argument(
'--max_nodes',
type=int,
default=1000,
help='Max total nodes in a single tree.'
)
parser.add_argument(
'--use_training_loss',
type=bool,
default=False,
help='If true, use training loss as termination criteria.'
)
FLAGS, unparsed = parser.parse_known_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
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