text: # 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 simplified deep MNIST classifier using convolutional layers.
  This script has the following changes when compared to mnist_deep.py:
  1. no dropout layer (which disables the rng op)
  2. no truncated normal initialzation(which disables the while op)
  
  See extensive documentation at
  https://www.tensorflow.org/get_started/mnist/pros
  """
  # Disable linter warnings to maintain consistency with tutorial.
  # pylint: disable=invalid-name
  # pylint: disable=g-bad-import-order
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  import tempfile
  import getpass
  import time
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  import tensorflow as tf
  import ngraph_bridge
  
  FLAGS = None
  
  
  def deepnn(x):
      """deepnn builds the graph for a deep net for classifying digits.
  
    Args:
      x: an input tensor with the dimensions (N_examples, 784), where 784 is the
      number of pixels in a standard MNIST image.
  
    Returns:
      A tuple (y, a scalar placeholder). y is a tensor of shape (N_examples, 10), with values
      equal to the logits of classifying the digit into one of 10 classes (the
      digits 0-9). The scalar placeholder is meant for the probability of dropout. Since we don't
      use a dropout layer in this script, this placeholder is of no relavance and acts as a dummy.
    """
      # Reshape to use within a convolutional neural net.
      # Last dimension is for "features" - there is only one here, since images are
      # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
      with tf.name_scope('reshape'):
          x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # First convolutional layer - maps one grayscale image to 32 feature maps.
      with tf.name_scope('conv1'):
          W_conv1 = weight_variable([5, 5, 1, 32], "W_conv1")
          b_conv1 = bias_variable([32])
          h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
      # Pooling layer - downsamples by 2X.
      with tf.name_scope('pool1'):
          h_pool1 = max_pool_2x2(h_conv1)
  
      # Second convolutional layer -- maps 32 feature maps to 64.
      with tf.name_scope('conv2'):
          W_conv2 = weight_variable([5, 5, 32, 64], "W_conv2")
          b_conv2 = bias_variable([64])
          h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
      # Second pooling layer.
      with tf.name_scope('pool2'):
          h_pool2 = max_pool_2x2(h_conv2)
  
      # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
      # is down to 7x7x64 feature maps -- maps this to 1024 features.
      with tf.name_scope('fc1'):
          W_fc1 = weight_variable([7 * 7 * 64, 1024], "W_fc1")
          b_fc1 = bias_variable([1024])
  
          h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
          h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Map the 1024 features to 10 classes, one for each digit
      with tf.name_scope('fc2'):
          W_fc2 = weight_variable([1024, 10], "W_fc2")
          b_fc2 = bias_variable([10])
  
          # y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
          y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
      return y_conv, tf.placeholder(tf.float32)
  
  
  def conv2d(x, W):
      """conv2d returns a 2d convolution layer with full stride."""
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  
  def max_pool_2x2(x):
      """max_pool_2x2 downsamples a feature map by 2X."""
      return tf.nn.max_pool(
          x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  
  
  def weight_variable(shape, name):
      """weight_variable generates a weight variable of a given shape."""
      weight_var = tf.get_variable(name, shape)
      return weight_var
  
  
  def bias_variable(shape):
      """bias_variable generates a bias variable of a given shape."""
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)
  
  
  def train_mnist_cnn(FLAGS):
      # Config
      config = tf.ConfigProto(
          allow_soft_placement=True,
          log_device_placement=False,
          inter_op_parallelism_threads=1)
      # Enable the custom optimizer using the rewriter config options
      config = ngraph_bridge.update_config(config)
  
      # Note: Additional configuration option to boost performance is to set the
      # following environment for the run:
      # OMP_NUM_THREADS=44 KMP_AFFINITY=granularity=fine,scatter
      # The OMP_NUM_THREADS number should correspond to the number of
      # cores in the system
  
      # Set Seed
      shuffle_batch = True
  
      if FLAGS.make_deterministic:
          seed = 1
          tf.random.set_random_seed(seed)
          shuffle_batch = False
  
      supported_optimizers = ["adam", "sgd", "momentum"]
  
      assert (FLAGS.optimizer in supported_optimizers), "Optimizer not supported"
  
      # Import data
      mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
  
      # Create the model
      x = tf.placeholder(tf.float32, [None, 784])
  
      # Define loss and optimizer
      y_ = tf.placeholder(tf.float32, [None, 10])
  
      # Build the graph for the deep net
      y_conv, keep_prob = deepnn(x)
  
      with tf.name_scope('loss'):
          cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
              labels=y_, logits=y_conv)
      cross_entropy = tf.reduce_mean(cross_entropy)
  
      optimizer_scope = FLAGS.optimizer + "_optimizer"
      with tf.name_scope(optimizer_scope):
          if FLAGS.optimizer == "adam":
              train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
          elif FLAGS.optimizer == "sgd":
              train_step = tf.train.GradientDescentOptimizer(5e-2).minimize(
                  cross_entropy)
          elif FLAGS.optimizer == "momentum":
              train_step = tf.train.MomentumOptimizer(1e-4,
                                                      0.9).minimize(cross_entropy)
  
      with tf.name_scope('accuracy'):
          correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
          correct_prediction = tf.cast(correct_prediction, tf.float32)
      accuracy = tf.reduce_mean(correct_prediction)
      tf.summary.scalar('Training accuracy', accuracy)
      tf.summary.scalar('Loss function', cross_entropy)
  
      graph_location = "/tmp/" + getpass.getuser(
      ) + "/tensorboard-logs/mnist-convnet"
      print('Saving graph to: %s' % graph_location)
  
      merged = tf.summary.merge_all()
      train_writer = tf.summary.FileWriter(graph_location)
      train_writer.add_graph(tf.get_default_graph())
  
      saver = tf.train.Saver()
  
      with tf.Session(config=config) as sess:
          sess.run(tf.global_variables_initializer())
          train_loops = FLAGS.train_loop_count
          loss_values = []
          for i in range(train_loops):
              batch = mnist.train.next_batch(
                  FLAGS.batch_size, shuffle=shuffle_batch)
              if i % 10 == 0:
                  t = time.time()
                  train_accuracy = accuracy.eval(feed_dict={
                      x: batch[0],
                      y_: batch[1],
                      keep_prob: 1.0
                  })
                  #tf.summary.scalar('Training accuracy', train_accuracy)
                  print('step %d, training accuracy %g, %g sec to evaluate' %
                        (i, train_accuracy, time.time() - t))
              t = time.time()
              _, summary, loss = sess.run([train_step, merged, cross_entropy],
                                          feed_dict={
                                              x: batch[0],
                                              y_: batch[1],
                                              keep_prob: 0.5
                                          })
              loss_values.append(loss)
              print('step %d, loss %g, %g sec for training step' %
                    (i, loss, time.time() - t))
              train_writer.add_summary(summary, i)
  
          print("Training finished. Running test")
  
          num_test_images = FLAGS.test_image_count
          x_test = mnist.test.images[:num_test_images]
          y_test = mnist.test.labels[:num_test_images]
  
          test_accuracy = accuracy.eval(feed_dict={
              x: x_test,
              y_: y_test,
              keep_prob: 1.0
          })
          print('test accuracy %g' % test_accuracy)
          saver.save(sess, FLAGS.model_dir)
          return loss_values, test_accuracy
  
  
  def main(_):
      train_mnist_cnn(FLAGS)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument(
          '--data_dir',
          type=str,
          default='/tmp/' + getpass.getuser() + 'tensorflow/mnist/input_data',
          help='Directory where input data is stored')
  
      parser.add_argument(
          '--train_loop_count',
          type=int,
          default=1000,
          help='Number of training iterations')
  
      parser.add_argument('--batch_size', type=int, default=50, help='Batch Size')
  
      parser.add_argument(
          '--test_image_count',
          type=int,
          default=None,
          help="Number of test images to evaluate on")
  
      parser.add_argument(
          '--make_deterministic',
          help="Fixes the random seed and stops shuffling.",
          action="store_true")
  
      parser.add_argument(
          '--model_dir',
          type=str,
          default='./mnist_trained/',
          help='enter model dir')
  
      parser.add_argument(
          '--optimizer',
          type=str,
          default='adam',
          help='Optimizer to use for training. Default: adam. Options: adam, sgd')
  
      FLAGS, unparsed = parser.parse_known_args()
      tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  