old_contents: from __future__ import unicode_literals, print_function
  
  from .optimizers import Eve, Adam, SGD, linear_decay
  from .util import minibatch
  
  import numpy.random
  import tqdm
  
  
  class Trainer(object):
      def __init__(self, model, train_data, L2=0.0):
          self.ops = model.ops
          self.model = model
          self.optimizer = Eve(Adam(model.ops, 0.001))
          self.batch_size = 128
          self.nb_epoch = 1
          self.i = 0
          self.L2 = 0.0
          self.dropout = 0.0
          self.dropout_decay = 0.0
          self._loss = 0.
          self.each_epoch = []
  
      def __enter__(self):
          return self, self.optimizer
  
      def __exit__(self, exc_type, exc_val, exc_tb):
          self.model.use_params(self.optimizer.averages)
  
      def iterate(self, train_X, train_y):
          orig_dropout = self.dropout
          for i in range(self.nb_epoch):
              indices = self.ops.xp.asarray(range(len(train_X)))
              numpy.random.shuffle(indices)
              j = 0
              while j < len(indices):
                  slice_ = indices[j : j + self.batch_size]
                  X = _take_slice(train_X, slice_)
                  y = _take_slice(train_y, slice_)
                  yield X, y
                  self.dropout = linear_decay(orig_dropout, self.dropout_decay,
                                              self.optimizer.nr_iter)
                  j += self.batch_size
              for func in self.each_epoch:
                  func()
  
  
  def _take_slice(data, slice_):
      x = [data[i] for i in slice_]
      return x
  
  
new_contents: from __future__ import unicode_literals, print_function
  
  from .optimizers import Eve, Adam, SGD, linear_decay
  from .util import minibatch
  
  import numpy.random
  from tqdm import tqdm
  
  
  class Trainer(object):
      def __init__(self, model, train_data, L2=0.0):
          self.ops = model.ops
          self.model = model
          self.optimizer = Eve(Adam(model.ops, 0.001))
          self.batch_size = 128
          self.nb_epoch = 1
          self.i = 0
          self.L2 = 0.0
          self.dropout = 0.0
          self.dropout_decay = 0.0
          self._loss = 0.
          self.each_epoch = []
  
      def __enter__(self):
          return self, self.optimizer
  
      def __exit__(self, exc_type, exc_val, exc_tb):
          self.model.use_params(self.optimizer.averages)
  
      def iterate(self, train_X, train_y):
          orig_dropout = self.dropout
          for i in range(self.nb_epoch):
              indices = self.ops.xp.asarray(range(len(train_X)))
              numpy.random.shuffle(indices)
              j = 0
              with tqdm(total=len(indices)) as pbar:
                  while j < len(indices):
                      slice_ = indices[j : j + self.batch_size]
                      X = _take_slice(train_X, slice_)
                      y = _take_slice(train_y, slice_)
                      yield X, y
                      self.dropout = linear_decay(orig_dropout, self.dropout_decay,
                                                  self.optimizer.nr_iter)
                      j += self.batch_size
                      pbar.update(self.batch_size)
              for func in self.each_epoch:
                  func()
  
  
  def _take_slice(data, slice_):
      x = [data[i] for i in slice_]
      return x
  
  
subject: Add progress bar to training