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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"""Train an MLP on MNIST using K-FAC.
See mlp.py for details.
"""
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.kfac.examples import mlp
FLAGS = None
def main(argv):
_ = argv
if FLAGS.use_estimator:
if FLAGS.num_towers != 1:
raise ValueError("Only 1 device supported in tf.estimator example.")
mlp.train_mnist_estimator(FLAGS.data_dir, num_epochs=200)
elif FLAGS.num_towers > 1:
mlp.train_mnist_multitower(
FLAGS.data_dir, num_epochs=200, num_towers=FLAGS.num_towers)
else:
mlp.train_mnist(FLAGS.data_dir, num_epochs=200)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
type=str,
default="/tmp/mnist",
help="Directory to store dataset in.")
parser.add_argument(
"--num_towers",
type=int,
default=1,
help="Number of CPUs to split minibatch across.")
parser.add_argument(
"--use_estimator",
action="store_true",
help="Use tf.estimator API to train.")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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