old_contents: # Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
  
  import numpy as np
  import tensorflow as tf
  
  # Creates simple random neural network that has softmax on output. No training.
  
  n_inputs = 5
  n_outputs = 3
  
  input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
  
  W = tf.Variable(tf.random.uniform([n_inputs, n_outputs]), name="weights")  # or just add uniform random
  b = tf.Variable(tf.random.uniform([n_outputs]), name="bias")
  Z = tf.matmul(input, W) + b
  
  hidden_layer = tf.nn.relu(Z)
  output_layer = tf.nn.softmax(hidden_layer, name="output")
  
  init = tf.global_variables_initializer()
  
  with tf.Session() as sess:
      init.run()
      export_path = "saved"
      builder = tf.saved_model.builder.SavedModelBuilder(export_path)
      signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':output_layer})
      builder.add_meta_graph_and_variables(sess,
                                           [tf.saved_model.tag_constants.SERVING],
                                           signature_def_map={'serving_default':signature})
      builder.save(as_text=True)
  
  
new_contents: # Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
  
  import numpy as np
  import tensorflow as tf
  
  # Creates simple random neural network that has softmax on output. No training.
  
  n_inputs = 5
  n_outputs = 3
  
  input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
  W = tf.Variable(tf.random.uniform([n_inputs, n_outputs]), name="weights")
  b = tf.Variable(tf.random.uniform([n_outputs]), name="bias")
  Z = tf.matmul(input, W) + b
  hidden_layer = tf.nn.relu(Z)
  output_layer = tf.nn.softmax(hidden_layer, name="output")
  
  init = tf.global_variables_initializer()
  
  with tf.Session() as sess:
      init.run()
      export_path = "saved"
      builder = tf.saved_model.builder.SavedModelBuilder(export_path)
      signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':output_layer})
      builder.add_meta_graph_and_variables(sess,
                                           [tf.saved_model.tag_constants.SERVING],
                                           signature_def_map={'serving_default':signature})
      builder.save(as_text=True)
  
  
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