text: #!/usr/bin/python
  #
  # Copyright 2020 Google LLC
  #
  # 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.
  """This tutorial comes from the Tensorflow MNIST quickstart at
  https://www.tensorflow.org/tutorials/quickstart/beginner.
  
  """
  import warnings
  
  import tensorflow as tf
  from absl import app, flags
  
  warnings.filterwarnings("ignore", category=DeprecationWarning)
  
  FLAGS = flags.FLAGS
  
  # Define a command-line argument using the Abseil library:
  # https://abseil.io/docs/python/guides/flags
  flags.DEFINE_float('learning_rate', 0.1, 'Learning rate.')
  flags.DEFINE_integer('epochs', 3, 'Epochs to train.')
  
  
  def get_keras_model(width=128, activation='relu'):
    """Returns an instance of a Keras Sequential model.
  https://www.tensorflow.org/api_docs/python/tf/keras/Sequential"""
    return tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(width, activation=activation),
        tf.keras.layers.Dense(width, activation=activation),
        tf.keras.layers.Dense(10, activation=None),
    ])
  
  
  def main(_):
    """Train a model against the MNIST dataset and print performance metrics."""
    mnist = tf.keras.datasets.mnist
  
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0
  
    model = get_keras_model()
  
    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    optimizer = tf.keras.optimizers.Adam(learning_rate=FLAGS.learning_rate)
  
    model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])
  
    print(
        f'Training model with learning rate={FLAGS.learning_rate} for {FLAGS.epochs} epochs.'
    )
    model.fit(x_train, y_train, epochs=FLAGS.epochs)
  
    print('Model performance: ')
    model.evaluate(x_test, y_test, verbose=2)
  
  
  if __name__ == '__main__':
    app.run(main)
  