1 files changed, 0 insertions, 93 deletions
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
deleted file mode 100644
index 3f4f794d2ac..00000000000
--- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-# Copyright 2015 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 very simple MNIST classifier.
-
-See extensive documentation at
-https://www.tensorflow.org/get_started/mnist/beginners
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import sys
-
-from tensorflow.examples.tutorials.mnist import input_data
-
-import tensorflow as tf
-
-FLAGS = None
-
-
-def main(_):
-  # Import data
-  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
-
-  # Create the model
-  x = tf.placeholder(tf.float32, [None, 784])
-
-  with tf.name_scope("layer"):
-    W = tf.Variable(tf.zeros([784, 10]))
-    b = tf.Variable(tf.zeros([10]))
-    y = tf.matmul(x, W) + b
-
-
-  # Define loss and optimizer
-  y_ = tf.placeholder(tf.float32, [None, 10])
-
-  # The raw formulation of cross-entropy,
-  #
-  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
-  #                                 reduction_indices=[1]))
-  #
-  # can be numerically unstable.
-  #
-  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
-  # outputs of 'y', and then average across the batch.
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
-  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
-
-  sess = tf.InteractiveSession()
-  tf.global_variables_initializer().run()
-  # Train
-  for _ in range(1000):
-    batch_xs, batch_ys = mnist.train.next_batch(100)
-    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
-
-  # Test trained model
-  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
-  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
-                                      y_: mnist.test.labels}))
-
-  # Save the model
-  export_path = "saved"
-  print('Exporting trained model to ', export_path)
-  builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-  signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y})
-  builder.add_meta_graph_and_variables(sess,
-                                       [tf.saved_model.tag_constants.SERVING],
-                                       signature_def_map={'serving_default':signature})
-  builder.save(as_text=True)
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
-                      help='Directory for storing input data')
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)