code: #  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.
  """Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app as absl_app
  from absl import flags
  flags.DEFINE_string(name="job-dir", default="/tmp", help="AI Platform Training passes this to the training script.")
  import tensorflow as tf  # pylint: disable=g-bad-import-order
  
  from official.mnist import dataset
  from official.utils.flags import core as flags_core
  from official.utils.logs import hooks_helper
  from official.utils.misc import distribution_utils
  from official.utils.misc import model_helpers
  
  
  LEARNING_RATE = 1e-4
  
  
  def create_model(data_format):
    """Model to recognize digits in the MNIST dataset.
  
    Network structure is equivalent to:
    https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
    and
    https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
  
    But uses the tf.keras API.
  
    Args:
      data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is
        typically faster on GPUs while 'channels_last' is typically faster on
        CPUs. See
        https://www.tensorflow.org/performance/performance_guide#data_formats
  
    Returns:
      A tf.keras.Model.
    """
    if data_format == 'channels_first':
      input_shape = [1, 28, 28]
    else:
      assert data_format == 'channels_last'
      input_shape = [28, 28, 1]
  
    l = tf.keras.layers
    max_pool = l.MaxPooling2D(
        (2, 2), (2, 2), padding='same', data_format=data_format)
    # The model consists of a sequential chain of layers, so tf.keras.Sequential
    # (a subclass of tf.keras.Model) makes for a compact description.
    return tf.keras.Sequential(
        [
            l.Reshape(
                target_shape=input_shape,
                input_shape=(28 * 28,)),
            l.Conv2D(
                32,
                5,
                padding='same',
                data_format=data_format,
                activation=tf.nn.relu),
            max_pool,
            l.Conv2D(
                64,
                5,
                padding='same',
                data_format=data_format,
                activation=tf.nn.relu),
            max_pool,
            l.Flatten(),
            l.Dense(1024, activation=tf.nn.relu),
            l.Dropout(0.4),
            l.Dense(10)
        ])
  
  
  def define_mnist_flags():
    flags_core.define_base()
    flags_core.define_performance(num_parallel_calls=False)
    flags_core.define_image()
    flags.adopt_module_key_flags(flags_core)
    flags_core.set_defaults(data_dir='/tmp/mnist_data',
                            model_dir='/tmp/mnist_model',
                            batch_size=100,
                            train_epochs=40)
  
  
  def model_fn(features, labels, mode, params):
    """The model_fn argument for creating an Estimator."""
    model = create_model(params['data_format'])
    image = features
    if isinstance(image, dict):
      image = features['image']
  
    if mode == tf.estimator.ModeKeys.PREDICT:
      logits = model(image, training=False)
      predictions = {
          'classes': tf.argmax(logits, axis=1),
          'probabilities': tf.nn.softmax(logits),
      }
      return tf.estimator.EstimatorSpec(
          mode=tf.estimator.ModeKeys.PREDICT,
          predictions=predictions,
          export_outputs={
              'classify': tf.estimator.export.PredictOutput(predictions)
          })
    if mode == tf.estimator.ModeKeys.TRAIN:
      optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
  
      logits = model(image, training=True)
      loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
      accuracy = tf.metrics.accuracy(
          labels=labels, predictions=tf.argmax(logits, axis=1))
  
      # Name tensors to be logged with LoggingTensorHook.
      tf.identity(LEARNING_RATE, 'learning_rate')
      tf.identity(loss, 'cross_entropy')
      tf.identity(accuracy[1], name='train_accuracy')
  
      # Save accuracy scalar to Tensorboard output.
      tf.summary.scalar('train_accuracy', accuracy[1])
  
      return tf.estimator.EstimatorSpec(
          mode=tf.estimator.ModeKeys.TRAIN,
          loss=loss,
          train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
    if mode == tf.estimator.ModeKeys.EVAL:
      logits = model(image, training=False)
      loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
      return tf.estimator.EstimatorSpec(
          mode=tf.estimator.ModeKeys.EVAL,
          loss=loss,
          eval_metric_ops={
              'accuracy':
                  tf.metrics.accuracy(
                      labels=labels, predictions=tf.argmax(logits, axis=1)),
          })
  
  
  def run_mnist(flags_obj):
    """Run MNIST training and eval loop.
  
    Args:
      flags_obj: An object containing parsed flag values.
    """
    model_helpers.apply_clean(flags_obj)
    model_function = model_fn
  
    session_config = tf.ConfigProto(
        inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
        intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
        allow_soft_placement=True)
  
    distribution_strategy = distribution_utils.get_distribution_strategy(
        flags_core.get_num_gpus(flags_obj), flags_obj.all_reduce_alg)
  
    run_config = tf.estimator.RunConfig(
        train_distribute=distribution_strategy, session_config=session_config)
  
    data_format = flags_obj.data_format
    if data_format is None:
      data_format = ('channels_first'
                     if tf.test.is_built_with_cuda() else 'channels_last')
    mnist_classifier = tf.estimator.Estimator(
        model_fn=model_function,
        model_dir=flags_obj.model_dir,
        config=run_config,
        params={
            'data_format': data_format,
        })
  
    # Set up training and evaluation input functions.
    def train_input_fn():
      """Prepare data for training."""
  
      # When choosing shuffle buffer sizes, larger sizes result in better
      # randomness, while smaller sizes use less memory. MNIST is a small
      # enough dataset that we can easily shuffle the full epoch.
      ds = dataset.train(flags_obj.data_dir)
      ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)
  
      # Iterate through the dataset a set number (`epochs_between_evals`) of times
      # during each training session.
      ds = ds.repeat(flags_obj.epochs_between_evals)
      return ds
  
    def eval_input_fn():
      return dataset.test(flags_obj.data_dir).batch(
          flags_obj.batch_size).make_one_shot_iterator().get_next()
  
    # Set up hook that outputs training logs every 100 steps.
    train_hooks = hooks_helper.get_train_hooks(
        flags_obj.hooks, model_dir=flags_obj.model_dir,
        batch_size=flags_obj.batch_size)
  
    # Train and evaluate model.
    for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
      mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
      eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
      print('\nEvaluation results:\n\t%s\n' % eval_results)
  
      if model_helpers.past_stop_threshold(flags_obj.stop_threshold,
                                           eval_results['accuracy']):
        break
  
    # Export the model
    if flags_obj.export_dir is not None:
      image = tf.placeholder(tf.float32, [None, 28, 28])
      input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
          'image': image,
      })
      mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn,
                                         strip_default_attrs=True)
  
  
  def main(_):
    run_mnist(flags.FLAGS)
  
  
  if __name__ == '__main__':
    tf.logging.set_verbosity(tf.logging.INFO)
    define_mnist_flags()
    absl_app.run(main)
  
apis: tensorflow.estimator.Estimator tensorflow.summary.scalar tensorflow.placeholder tensorflow.logging.set_verbosity tensorflow.train.AdamOptimizer tensorflow.estimator.export.build_raw_serving_input_receiver_fn tensorflow.test.is_built_with_cuda tensorflow.estimator.RunConfig tensorflow.losses.sparse_softmax_cross_entropy tensorflow.estimator.export.PredictOutput tensorflow.argmax tensorflow.train.get_or_create_global_step tensorflow.identity tensorflow.nn.softmax tensorflow.ConfigProto