code: def train(
      sess,
      loss,
      x_train,
      y_train,
      init_all=False,
      evaluate=None,
      feed=None,
      args=None,
      rng=None,
      var_list=None,
      fprop_args=None,
      optimizer=None,
      devices=None,
      x_batch_preprocessor=None,
      use_ema=False,
      ema_decay=0.998,
      run_canary=None,
      loss_threshold=1e5,
      dataset_train=None,
      dataset_size=None,
  ):
      """
      Run (optionally multi-replica, synchronous) training to minimize `loss`
      :param sess: TF session to use when training the graph
      :param loss: tensor, the loss to minimize
      :param x_train: numpy array with training inputs or tf Dataset
      :param y_train: numpy array with training outputs or tf Dataset
      :param init_all: (boolean) If set to true, all TF variables in the session
                       are (re)initialized, otherwise only previously
                       uninitialized variables are initialized before training.
      :param evaluate: function that is run after each training iteration
                       (typically to display the test/validation accuracy).
      :param feed: An optional dictionary that is appended to the feeding
                   dictionary before the session runs. Can be used to feed
                   the learning phase of a Keras model for instance.
      :param args: dict or argparse `Namespace` object.
                   Should contain `nb_epochs`, `learning_rate`,
                   `batch_size`
      :param rng: Instance of numpy.random.RandomState
      :param var_list: Optional list of parameters to train.
      :param fprop_args: dict, extra arguments to pass to fprop (loss and model).
      :param optimizer: Optimizer to be used for training
      :param devices: list of device names to use for training
          If None, defaults to: all GPUs, if GPUs are available
                                all devices, if no GPUs are available
      :param x_batch_preprocessor: callable
          Takes a single tensor containing an x_train batch as input
          Returns a single tensor containing an x_train batch as output
          Called to preprocess the data before passing the data to the Loss
      :param use_ema: bool
          If true, uses an exponential moving average of the model parameters
      :param ema_decay: float or callable
          The decay parameter for EMA, if EMA is used
          If a callable rather than a float, this is a callable that takes
          the epoch and batch as arguments and returns the ema_decay for
          the current batch.
      :param loss_threshold: float
          Raise an exception if the loss exceeds this value.
          This is intended to rapidly detect numerical problems.
          Sometimes the loss may legitimately be higher than this value. In
          such cases, raise the value. If needed it can be np.inf.
      :param dataset_train: tf Dataset instance.
          Used as a replacement for x_train, y_train for faster performance.
        :param dataset_size: integer, the size of the dataset_train.
      :return: True if model trained
      """
  
      # Check whether the hardware is working correctly
      canary.run_canary()
      if run_canary is not None:
          warnings.warn(
              "The `run_canary` argument is deprecated. The canary "
              "is now much cheaper and thus runs all the time. The "
              "canary now uses its own loss function so it is not "
              "necessary to turn off the canary when training with "
              " a stochastic loss. Simply quit passing `run_canary`."
              "Passing `run_canary` may become an error on or after "
              "2019-10-16."
          )
  
      args = _ArgsWrapper(args or {})
      fprop_args = fprop_args or {}
  
      # Check that necessary arguments were given (see doc above)
      # Be sure to support 0 epochs for debugging purposes
      if args.nb_epochs is None:
          raise ValueError("`args` must specify number of epochs")
      if optimizer is None:
          if args.learning_rate is None:
              raise ValueError(
                  "Learning rate was not given in args dict"
              )
      assert args.batch_size, "Batch size was not given in args dict"
  
      if rng is None:
          rng = np.random.RandomState()
  
      if optimizer is None:
          optimizer = tf.train.AdamOptimizer(
              learning_rate=args.learning_rate
          )
      else:
          if not isinstance(optimizer, tf.train.Optimizer):
              raise ValueError(
                  "optimizer object must be from a child class of "
                  "tf.train.Optimizer"
              )
  
      grads = []
      xs = []
      preprocessed_xs = []
      ys = []
      if dataset_train is not None:
          assert (
              x_train is None
              and y_train is None
              and x_batch_preprocessor is None
          )
          if dataset_size is None:
              raise ValueError("You must provide a dataset size")
          data_iterator = (
              dataset_train.make_one_shot_iterator().get_next()
          )
          x_train, y_train = sess.run(data_iterator)
  
      devices = infer_devices(devices)
      for device in devices:
          with tf.device(device):
              x = tf.placeholder(
                  x_train.dtype, (None,) + x_train.shape[1:]
              )
              y = tf.placeholder(
                  y_train.dtype, (None,) + y_train.shape[1:]
              )
              xs.append(x)
              ys.append(y)
  
              if x_batch_preprocessor is not None:
                  x = x_batch_preprocessor(x)
  
              # We need to keep track of these so that the canary can feed
              # preprocessed values. If the canary had to feed raw values,
              # stochastic preprocessing could make the canary fail.
              preprocessed_xs.append(x)
  
              loss_value = loss.fprop(x, y, **fprop_args)
  
              grads.append(
                  optimizer.compute_gradients(
                      loss_value, var_list=var_list
                  )
              )
      num_devices = len(devices)
      print("num_devices: ", num_devices)
  
      grad = avg_grads(grads)
      # Trigger update operations within the default graph (such as batch_norm).
      with tf.control_dependencies(
          tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      ):
          train_step = optimizer.apply_gradients(grad)
  
      epoch_tf = tf.placeholder(tf.int32, [])
      batch_tf = tf.placeholder(tf.int32, [])
  
      if use_ema:
          if callable(ema_decay):
              ema_decay = ema_decay(epoch_tf, batch_tf)
          ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
          with tf.control_dependencies([train_step]):
              train_step = ema.apply(var_list)
          # Get pointers to the EMA's running average variables
          avg_params = [ema.average(param) for param in var_list]
          # Make temporary buffers used for swapping the live and running average
          # parameters
          tmp_params = [
              tf.Variable(param, trainable=False) for param in var_list
          ]
          # Define the swapping operation
          param_to_tmp = [
              tf.assign(tmp, param)
              for tmp, param in safe_zip(tmp_params, var_list)
          ]
          with tf.control_dependencies(param_to_tmp):
              avg_to_param = [
                  tf.assign(param, avg)
                  for param, avg in safe_zip(var_list, avg_params)
              ]
          with tf.control_dependencies(avg_to_param):
              tmp_to_avg = [
                  tf.assign(avg, tmp)
                  for avg, tmp in safe_zip(avg_params, tmp_params)
              ]
          swap = tmp_to_avg
  
      batch_size = args.batch_size
  
      assert batch_size % num_devices == 0
      device_batch_size = batch_size // num_devices
  
      if init_all:
          sess.run(tf.global_variables_initializer())
      else:
          initialize_uninitialized_global_variables(sess)
  
      for epoch in xrange(args.nb_epochs):
          if dataset_train is not None:
              nb_batches = int(
                  math.ceil(float(dataset_size) / batch_size)
              )
          else:
              # Indices to shuffle training set
              index_shuf = list(range(len(x_train)))
              # Randomly repeat a few training examples each epoch to avoid
              # having a too-small batch
              while len(index_shuf) % batch_size != 0:
                  index_shuf.append(rng.randint(len(x_train)))
              nb_batches = len(index_shuf) // batch_size
              rng.shuffle(index_shuf)
              # Shuffling here versus inside the loop doesn't seem to affect
              # timing very much, but shuffling here makes the code slightly
              # easier to read
              x_train_shuffled = x_train[index_shuf]
              y_train_shuffled = y_train[index_shuf]
  
          prev = time.time()
          for batch in range(nb_batches):
              if dataset_train is not None:
                  x_train_shuffled, y_train_shuffled = sess.run(
                      data_iterator
                  )
                  start, end = 0, batch_size
              else:
                  # Compute batch start and end indices
                  start = batch * batch_size
                  end = (batch + 1) * batch_size
                  # Perform one training step
                  diff = end - start
                  assert diff == batch_size
  
              feed_dict = {epoch_tf: epoch, batch_tf: batch}
              for dev_idx in xrange(num_devices):
                  cur_start = start + dev_idx * device_batch_size
                  cur_end = start + (dev_idx + 1) * device_batch_size
                  feed_dict[xs[dev_idx]] = x_train_shuffled[
                      cur_start:cur_end
                  ]
                  feed_dict[ys[dev_idx]] = y_train_shuffled[
                      cur_start:cur_end
                  ]
              if cur_end != end and dataset_train is None:
                  msg = (
                      "batch_size (%d) must be a multiple of num_devices "
                      "(%d).\nCUDA_VISIBLE_DEVICES: %s"
                      "\ndevices: %s"
                  )
                  args = (
                      batch_size,
                      num_devices,
                      os.environ["CUDA_VISIBLE_DEVICES"],
                      str(devices),
                  )
                  raise ValueError(msg % args)
              if feed is not None:
                  feed_dict.update(feed)
  
              _, loss_numpy = sess.run(
                  [train_step, loss_value], feed_dict=feed_dict
              )
  
              if np.abs(loss_numpy) > loss_threshold:
                  raise ValueError(
                      "Extreme loss during training: ", loss_numpy
                  )
              if np.isnan(loss_numpy) or np.isinf(loss_numpy):
                  raise ValueError("NaN/Inf loss during training")
          assert dataset_train is not None or end == len(
              index_shuf
          )  # Check that all examples were used
          cur = time.time()
          _logger.info(
              "Epoch "
              + str(epoch)
              + " took "
              + str(cur - prev)
              + " seconds"
          )
          if evaluate is not None:
              if use_ema:
                  # Before running evaluation, load the running average
                  # parameters into the live slot, so we can see how well
                  # the EMA parameters are performing
                  sess.run(swap)
              evaluate()
              if use_ema:
                  # Swap the parameters back, so that we continue training
                  # on the live parameters
                  sess.run(swap)
      if use_ema:
          # When training is done, swap the running average parameters into
          # the live slot, so that we use them when we deploy the model
          sess.run(swap)
  
      return True
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/train.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import logging
  import os
  import time
  import warnings
  import math
  import numpy
  from six.moves import xrange
  import tensorflow
  from cleverhans import canary
  from cleverhans.utils import _ArgsWrapper, create_logger
  from cleverhans.utils import safe_zip
  from cleverhans.utils_tf import infer_devices
  from cleverhans.utils_tf import initialize_uninitialized_global_variables
label_desc: Use image node_id 1 for calling a global function with example usage: train(sess, loss, x_train, y_train, init_all, evaluate, feed, args, rng, var_list, fprop_args, optimizer, devices, x_batch_preprocessor, use_ema, ema_decay, run_canary, loss_threshold, dataset_train, dataset_size) and returns: True