1 files changed, 0 insertions, 64 deletions
diff --git a/tensorflow/contrib/kfac/examples/mlp_mnist_main.py b/tensorflow/contrib/kfac/examples/mlp_mnist_main.py
deleted file mode 100644
index 9c34ade1d2..0000000000
--- a/tensorflow/contrib/kfac/examples/mlp_mnist_main.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# 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)