import os.path

    import re

    import time

    import numpy as np

    import tensorflow as tf

    import cifar10

    batch_size=128

    #train_dir='/tmp/cifar10_train'

    max_steps=1000000

    num_gpus=4

    #log_device_placement=False

    def tower_loss(scope):

      """Calculate the total loss on a single tower running the CIFAR model.

      Args:

        scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'

      Returns:

         Tensor of shape [] containing the total loss for a batch of data

      """

      # Get images and labels for CIFAR-10.

      images, labels = cifar10.distorted_inputs()

      # Build inference Graph.

      logits = cifar10.inference(images)

      # Build the portion of the Graph calculating the losses. Note that we will

      # assemble the total_loss using a custom function below.

      _ = cifar10.loss(logits, labels)

      # Assemble all of the losses for the current tower only.

      losses = tf.get_collection('losses', scope)

      # Calculate the total loss for the current tower.

      total_loss = tf.add_n(losses, name='total_loss')

      # Compute the moving average of all individual losses and the total loss.

      # loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')

      # loss_averages_op = loss_averages.apply(losses + [total_loss])

      # Attach a scalar summary to all individual losses and the total loss; do the

      # same for the averaged version of the losses.

      # for l in losses + [total_loss]:

        # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training

        # session. This helps the clarity of presentation on tensorboard.

        # loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)

        # Name each loss as '(raw)' and name the moving average version of the loss

        # as the original loss name.

        # tf.scalar_summary(loss_name +' (raw)', l)

        # tf.scalar_summary(loss_name, loss_averages.average(l))

        # with tf.control_dependencies([loss_averages_op]):

        # total_loss = tf.identity(total_loss)

      return total_loss

    def average_gradients(tower_grads):

      """Calculate the average gradient for each shared variable across all towers.

      Note that this function provides a synchronization point across all towers.

      Args:

        tower_grads: List of lists of (gradient, variable) tuples. The outer list

          is over individual gradients. The inner list is over the gradient

          calculation for each tower.

      Returns:

         List of pairs of (gradient, variable) where the gradient has been averaged

         across all towers.

      """

      average_grads = []

      for grad_and_vars in zip(*tower_grads):

        # Note that each grad_and_vars looks like the following:

        #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))

        grads = []

        for g, _ in grad_and_vars:

          # Add 0 dimension to the gradients to represent the tower.

          expanded_g = tf.expand_dims(g, 0)

          # Append on a 'tower' dimension which we will average over below.

          grads.append(expanded_g)

        # Average over the 'tower' dimension.

        grad = tf.concat(grads, 0)

        grad = tf.reduce_mean(grad, 0)

        # Keep in mind that the Variables are redundant because they are shared

        # across towers. So .. we will just return the first tower's pointer to

        # the Variable.

        v = grad_and_vars[0][1]

        grad_and_var = (grad, v)

        average_grads.append(grad_and_var)

      return average_grads

    def train():

      """Train CIFAR-10 for a number of steps."""

      with tf.Graph().as_default(), tf.device('/cpu:0'):

        # Create a variable to count the number of train() calls. This equals the

        # number of batches processed * FLAGS.num_gpus.

        global_step = tf.get_variable(

            'global_step', [],

            initializer=tf.constant_initializer(0), trainable=False)

        # Calculate the learning rate schedule.

        num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /

                             batch_size)

        decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)

        # Decay the learning rate exponentially based on the number of steps.

        lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,

                                        global_step,

                                        decay_steps,

                                        cifar10.LEARNING_RATE_DECAY_FACTOR,

                                        staircase=True)

        # Create an optimizer that performs gradient descent.

        opt = tf.train.GradientDescentOptimizer(lr)

        # Calculate the gradients for each model tower.

        tower_grads = []

        for i in range(num_gpus):

          with tf.device('/gpu:%d' % i):

            with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:

              # Calculate the loss for one tower of the CIFAR model. This function

              # constructs the entire CIFAR model but shares the variables across

              # all towers.

              loss = tower_loss(scope)

              # Reuse variables for the next tower.

              tf.get_variable_scope().reuse_variables()

              # Retain the summaries from the final tower.

              # summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

              # Calculate the gradients for the batch of data on this CIFAR tower.

              grads = opt.compute_gradients(loss)

              # Keep track of the gradients across all towers.

              tower_grads.append(grads)

        # We must calculate the mean of each gradient. Note that this is the

        # synchronization point across all towers.

        grads = average_gradients(tower_grads)

        # Add a summary to track the learning rate.

        # summaries.append(tf.scalar_summary('learning_rate', lr))

        # Add histograms for gradients.

        # for grad, var in grads:

        #     if grad is not None:

        #         summaries.append(

        #             tf.histogram_summary(var.op.name + '/gradients', grad))

        # Apply the gradients to adjust the shared variables.

        apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

        # Add histograms for trainable variables.

        # for var in tf.trainable_variables():

        #     summaries.append(tf.histogram_summary(var.op.name, var))

        # Track the moving averages of all trainable variables.

        # variable_averages = tf.train.ExponentialMovingAverage(

        #     cifar10.MOVING_AVERAGE_DECAY, global_step)

        # variables_averages_op = variable_averages.apply(tf.trainable_variables())

        # Group all updates to into a single train op.

        # train_op = tf.group(apply_gradient_op, variables_averages_op)

        # Create a saver.

        saver = tf.train.Saver(tf.all_variables())

        # Build the summary operation from the last tower summaries.

        # summary_op = tf.merge_summary(summaries)

        # Build an initialization operation to run below.

        init = tf.global_variables_initializer()

        # Start running operations on the Graph. allow_soft_placement must be set to

        # True to build towers on GPU, as some of the ops do not have GPU

        # implementations.

        sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))

        sess.run(init)

        # Start the queue runners.

        tf.train.start_queue_runners(sess=sess)

        # summary_writer = tf.train.SummaryWriter(train_dir, sess.graph)

        for step in range(max_steps):

          start_time = time.time()

          _, loss_value = sess.run([apply_gradient_op, loss])

          duration = time.time() - start_time

          assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

          if step % 10 == 0:

            num_examples_per_step = batch_size * num_gpus

            examples_per_sec = num_examples_per_step / duration

            sec_per_batch = duration / num_gpus

            format_str = ('step %d, loss = %.2f (%.1f examples/sec; %.3f '

                          'sec/batch)')

            print (format_str % (step, loss_value,

                                 examples_per_sec, sec_per_batch))

            # if step % 100 == 0:

            #     summary_str = sess.run(summary_op)

            #     summary_writer.add_summary(summary_str, step)

          # Save the model checkpoint periodically.

          if step % 1000 == 0 or (step + 1) == max_steps:

          # checkpoint_path = os.path.join(train_dir, 'model.ckpt')

            saver.save(sess, '/tmp/cifar10_train/model.ckpt', global_step=step)

    cifar10.maybe_download_and_extract()

    #if tf.gfile.Exists(train_dir):

    #  tf.gfile.DeleteRecursively(train_dir)

    #tf.gfile.MakeDirs(train_dir)

    train()