text: from __future__ import print_function
  from __future__ import absolute_import
  
  import warnings
  import copy
  import time
  import numpy as np
  import threading
  try:
      import queue
  except ImportError:
      import Queue as queue
  
  from .topology import Container
  from .. import backend as K
  from .. import optimizers
  from .. import objectives
  from .. import metrics as metrics_module
  from ..utils.generic_utils import Progbar
  from .. import callbacks as cbks
  
  
  def standardize_input_data(data, names, shapes=None, check_batch_dim=True,
                             exception_prefix=''):
      '''Users may pass data as a list of arrays, dictionary of arrays,
      or as a single array. We normalize this to an ordered list of
      arrays (same order as `names`), while checking that the provided
      arrays have shapes that match the network's expectations.
      '''
      if type(data) is dict:
          arrays = []
          for name in names:
              if name not in data:
                  raise Exception('No data provided for "' +
                                  name + '". Need data for each key in: ' +
                                  str(data.keys()))
              arrays.append(data[name])
      elif type(data) is list:
          if len(data) != len(names):
              if len(data) > 0 and hasattr(data[0], 'shape'):
                  raise Exception('Error when checking ' + exception_prefix +
                                  ': the list of Numpy arrays '
                                  'that you are passing to your model '
                                  'is not the size the model expected. '
                                  'Expected to see ' + str(len(names)) +
                                  ' arrays but instead got '
                                  'the following list of ' + str(len(data)) +
                                  ' arrays: ' + str(data)[:200] +
                                  '...')
              else:
                  if len(names) == 1:
                      data = [np.asarray(data)]
                  else:
                      raise Exception('Error when checking ' + exception_prefix +
                                      ': you are passing a list as '
                                      'input to your model, '
                                      'but the model expects a '
                                      'a list of ' + str(len(names)) +
                                      ' Numpy arrays instead. '
                                      'The list you passed was: ' +
                                      str(data)[:200])
          arrays = data
      else:
          if not hasattr(data, 'shape'):
              raise Exception('Error when checking ' + exception_prefix +
                              ': data should be a Numpy array, '
                              'or list/dict of Numpy arrays. '
                              'Found: ' + str(data)[:200] + '...')
          if len(names) != 1:
              # case: model expects multiple inputs but only received
              # a single Numpy array
              raise Exception('The model expects ' + str(len(names)) +
                              ' input arrays, but only received one array. '
                              'Found: array with shape ' + str(data.shape))
          arrays = [data]
  
      # make arrays at least 2D
      for i in range(len(names)):
          array = arrays[i]
          if len(array.shape) == 1:
              array = np.expand_dims(array, 1)
              arrays[i] = array
  
      # check shapes compatibility
      if shapes:
          for i in range(len(names)):
              array = arrays[i]
              if len(array.shape) != len(shapes[i]):
                  raise Exception('Error when checking ' + exception_prefix +
                                  ': expected ' + names[i] +
                                  ' to have ' + str(len(shapes[i])) +
                                  ' dimensions, but got array with shape ' +
                                  str(array.shape))
              for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
                  if not j and not check_batch_dim:
                      # skip the first axis
                      continue
                  if ref_dim:
                      if ref_dim != dim:
                          raise Exception('Error when checking ' + exception_prefix +
                                          ': expected ' + names[i] +
                                          ' to have shape ' + str(shapes[i]) +
                                          ' but got array with shape ' +
                                          str(array.shape))
      return arrays
  
  
  def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
      if x_weight is None or len(x_weight) == 0:
          return [None for _ in output_names]
      if len(output_names) == 1:
          if type(x_weight) is list and len(x_weight) == 1:
              return x_weight
          if type(x_weight) is dict and output_names[0] in x_weight:
              return [x_weight[output_names[0]]]
          else:
              return [x_weight]
      if type(x_weight) is list:
          if len(x_weight) != len(output_names):
              raise Exception('Provided `' + weight_type + '` was a list of ' +
                              str(len(x_weight)) +
                              ' elements, but the model has ' +
                              str(len(output_names)) + ' outputs. '
                              'You should provide one `' + weight_type + '`'
                              'array per model output.')
          return x_weight
      if type(x_weight) is dict:
          x_weights = []
          for name in output_names:
              x_weights.append(x_weight.get(name))
          return x_weights
      else:
          raise Exception('The model has multiple outputs, so `' +
                          weight_type + '` '
                          'should be either a list of a dict. '
                          'Provided `' + weight_type +
                          '` type not understood: ' +
                          str(x_weight))
  
  
  def standardize_class_weights(class_weight, output_names):
      return standardize_sample_or_class_weights(class_weight,
                                                 output_names,
                                                 'class_weight')
  
  
  def standardize_sample_weights(sample_weight, output_names):
      return standardize_sample_or_class_weights(sample_weight,
                                                 output_names,
                                                 'sample_weight')
  
  
  def check_array_lengths(X, Y, W):
      x_lengths = [x.shape[0] for x in X]
      y_lengths = [y.shape[0] for y in Y]
      w_lengths = [w.shape[0] for w in W]
      set_x = set(x_lengths)
      if len(set_x) != 1:
          raise Exception('All input arrays (x) should have '
                          'the same number of samples.')
      set_y = set(y_lengths)
      if len(set_y) != 1:
          raise Exception('All target arrays (y) should have '
                          'the same number of samples.')
      set_w = set(w_lengths)
      if len(set_w) != 1:
          raise Exception('All sample_weight arrays should have '
                          'the same number of samples.')
      if list(set_x)[0] != list(set_y)[0]:
          raise Exception('Input arrays should have '
                          'the same number of samples as target arrays. Found ' +
                          str(list(set_x)[0]) + ' input samples and ' +
                          str(list(set_y)[0]) + ' target samples.')
      if list(set_x)[0] != list(set_w)[0]:
          raise Exception('Sample_weight arrays should have '
                          'the same number of samples as input arrays. Found ' +
                          str(list(set_x)[0]) + ' input samples and ' +
                          str(list(set_w)[0]) + ' target samples.')
  
  
  def check_loss_and_target_compatibility(targets, losses, output_shapes):
      assert len(targets) == len(losses) == len(output_shapes)
      key_losses = {'mean_square_error',
                    'binary_crossentropy',
                    'categorical_crossentropy'}
      for y, loss, shape in zip(targets, losses, output_shapes):
          if loss.__name__ == 'categorical_crossentropy':
              if y.shape[1] == 1:
                  raise Exception('You are passing a target array of shape ' + str(y.shape) +
                                  ' while using as loss `categorical_crossentropy`. '
                                  '`categorical_crossentropy` expects '
                                  'targets to be binary matrices (1s and 0s) '
                                  'of shape (samples, classes). '
                                  'If your targets are integer classes, '
                                  'you can convert them to the expected format via:\n'
                                  '```\n'
                                  'from keras.utils.np_utils import to_categorical\n'
                                  'y_binary = to_categorical(y_int)\n'
                                  '```\n'
                                  '\n'
                                  'Alternatively, you can use the loss function '
                                  '`sparse_categorical_crossentropy` instead, '
                                  'which does expect integer targets.')
          if loss.__name__ in key_losses and shape[1] is not None and y.shape[1] != shape[1]:
                  raise Exception('A target array with shape ' + str(y.shape) +
                                  ' was passed for an output of shape ' + str(shape) +
                                  ' while using as loss `' + loss.__name__ + '`. '
                                  'This loss expects '
                                  'targets to have the same shape '
                                  'as the output.')
  
  
  def collect_metrics(metrics, output_names):
      if not metrics:
          return [[] for _ in output_names]
      if type(metrics) is list:
          # we then apply all metrics to all outputs.
          return [copy.copy(metrics) for _ in output_names]
      elif type(metrics) is dict:
          nested_metrics = []
          for name in output_names:
              output_metrics = metrics.get(name, [])
              if type(output_metrics) is not list:
                  output_metrics = [output_metrics]
              nested_metrics.append(output_metrics)
          return nested_metrics
      else:
          raise Exception('Type of `metrics` argument not understood. '
                          'Expected a list or dictionary, found: ' +
                          str(metrics))
  
  
  def collect_trainable_weights(layer):
      trainable = getattr(layer, 'trainable', True)
      if not trainable:
          return []
      weights = []
      if layer.__class__.__name__ in ['Sequential', 'Model']:
          for sublayer in layer.layers:
              weights += collect_trainable_weights(sublayer)
      elif layer.__class__.__name__ == 'Graph':
          for sublayer in layer._graph_nodes.values():
              weights += collect_trainable_weights(sublayer)
      else:
          weights += layer.trainable_weights
      return weights
  
  
  def batch_shuffle(index_array, batch_size):
      '''This shuffles an array in a batch-wise fashion.
      Useful for shuffling HDF5 arrays
      (where one cannot access arbitrary indices).
      '''
      batch_count = int(len(index_array) / batch_size)
      # to reshape we need to be cleanly divisible by batch size
      # we stash extra items and reappend them after shuffling
      last_batch = index_array[batch_count * batch_size:]
      index_array = index_array[:batch_count * batch_size]
      index_array = index_array.reshape((batch_count, batch_size))
      np.random.shuffle(index_array)
      index_array = index_array.flatten()
      return np.append(index_array, last_batch)
  
  
  def make_batches(size, batch_size):
      '''Returns a list of batch indices (tuples of indices).
      '''
      nb_batch = int(np.ceil(size / float(batch_size)))
      return [(i * batch_size, min(size, (i + 1) * batch_size))
              for i in range(0, nb_batch)]
  
  
  def slice_X(X, start=None, stop=None):
      '''This takes an array-like, or a list of
      array-likes, and outputs:
          - X[start:stop] if X is an array-like
          - [x[start:stop] for x in X] if X in a list
  
      Can also work on list/array of indices: `slice_X(x, indices)`
  
      # Arguments:
          start: can be an integer index (start index)
              or a list/array of indices
          stop: integer (stop index); should be None if
              `start` was a list.
      '''
      if type(X) == list:
          if hasattr(start, '__len__'):
              # hdf5 datasets only support list objects as indices
              if hasattr(start, 'shape'):
                  start = start.tolist()
              return [x[start] for x in X]
          else:
              return [x[start:stop] for x in X]
      else:
          if hasattr(start, '__len__'):
              if hasattr(start, 'shape'):
                  start = start.tolist()
              return X[start]
          else:
              return X[start:stop]
  
  
  def weighted_objective(fn):
      '''Transforms an objective function `fn(y_true, y_pred)`
      into a sample-weighted, cost-masked objective function
      `fn(y_true, y_pred, weights, mask)`.
      '''
      def weighted(y_true, y_pred, weights, mask=None):
          # score_array has ndim >= 2
          score_array = fn(y_true, y_pred)
          if mask is not None:
              # Cast the mask to floatX to avoid float64 upcasting in theano
              mask = K.cast(mask, K.floatx())
              # mask should have the same shape as score_array
              score_array *= mask
              #  the loss per batch should be proportional
              #  to the number of unmasked samples.
              score_array /= K.mean(mask)
  
          # reduce score_array to same ndim as weight array
          ndim = K.ndim(score_array)
          weight_ndim = K.ndim(weights)
          score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
  
          # apply sample weighting
          if weights is not None:
              score_array *= weights
              score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
          return K.mean(score_array)
      return weighted
  
  
  def standardize_weights(y, sample_weight=None, class_weight=None,
                          sample_weight_mode=None):
      '''Performs weight input validation and standardization
      to a single sample-wise (or timestep-wise) weight array.
      '''
      if sample_weight_mode is not None:
          if sample_weight_mode != 'temporal':
              raise Exception('"sample_weight_mode '
                              'should be None or "temporal". '
                              'Found: ' + str(sample_weight_mode))
          if len(y.shape) < 3:
              raise Exception('Found a sample_weight array for '
                              'an input with shape ' +
                              str(y.shape) + '. '
                              'Timestep-wise sample weighting (use of '
                              'sample_weight_mode="temporal") is restricted to '
                              'outputs that are at least 3D, i.e. that have '
                              'a time dimension.')
          if sample_weight is not None and len(sample_weight.shape) != 2:
              raise Exception('Found a sample_weight array with shape ' +
                              str(sample_weight.shape) + '. '
                              'In order to use timestep-wise sample weighting, '
                              'you should pass a 2D sample_weight array.')
      else:
          if sample_weight is not None and len(sample_weight.shape) != 1:
              raise Exception('Found a sample_weight array with shape ' +
                              str(sample_weight.shape) + '. '
                              'In order to use timestep-wise sample weights, '
                              'you should specify sample_weight_mode="temporal" '
                              'in compile(). If you just mean to use '
                              'sample-wise weights, make sure your '
                              'sample_weight array is 1D.')
  
      if sample_weight is not None:
          assert len(sample_weight.shape) <= len(y.shape)
          # TODO: proper error message
          assert y.shape[:sample_weight.ndim] == sample_weight.shape
          return sample_weight
      elif isinstance(class_weight, dict):
          if len(y.shape) > 2:
              raise Exception('class_weight not supported for '
                              '3+ dimensional targets.')
          if y.shape[1] > 1:
              y_classes = y.argmax(axis=1)
          elif y.shape[1] == 1:
              y_classes = np.reshape(y, y.shape[0])
          else:
              y_classes = y
          weights = np.asarray([class_weight[cls] for cls in y_classes])
          return weights
      else:
          if sample_weight_mode is None:
              return np.ones((y.shape[0],))
          else:
              return np.ones((y.shape[0], y.shape[1]))
  
  
  def generator_queue(generator, max_q_size=10,
                      wait_time=0.05, nb_worker=1):
      '''Builds a threading queue out of a data generator.
      Used in `fit_generator`, `evaluate_generator`, `predict_generator`.
      '''
      q = queue.Queue()
      _stop = threading.Event()
  
      def data_generator_task():
          while not _stop.is_set():
              try:
                  if q.qsize() < max_q_size:
                      try:
                          generator_output = next(generator)
                      except ValueError:
                          continue
                      q.put(generator_output)
                  else:
                      time.sleep(wait_time)
              except Exception:
                  _stop.set()
                  raise
  
      generator_threads = [threading.Thread(target=data_generator_task)
                           for _ in range(nb_worker)]
  
      for thread in generator_threads:
          thread.daemon = True
          thread.start()
  
      return q, _stop
  
  
  class Model(Container):
  
      def compile(self, optimizer, loss, metrics=[], loss_weights=None,
                  sample_weight_mode=None, **kwargs):
          '''Configures the model for training.
  
          # Arguments
              optimizer: str (name of optimizer) or optimizer object.
                  See [optimizers](/optimizers).
              loss: str (name of objective function) or objective function.
                  See [objectives](/objectives).
                  If the model has multiple outputs, you can use a different loss
                  on each output by passing a dictionary or a list of objectives.
              metrics: list of metrics to be evaluated by the model
                  during training and testing.
                  Typically you will use `metrics=['accuracy']`.
                  To specify different metrics for different outputs of a
                  multi-output model, you could also pass a dictionary,
                  such as `metrics={'output_a': 'accuracy'}`.
              sample_weight_mode: if you need to do timestep-wise
                  sample weighting (2D weights), set this to "temporal".
                  "None" defaults to sample-wise weights (1D).
                  If the model has multiple outputs, you can use a different
                  `sample_weight_mode` on each output by passing a
                  dictionary or a list of modes.
              kwargs: when using the Theano backend, these arguments
                  are passed into K.function. Ignored for Tensorflow backend.
          '''
          self.optimizer = optimizers.get(optimizer)
          self.sample_weight_mode = sample_weight_mode
          self.loss = loss
          self.loss_weights = loss_weights
  
          # prepare loss weights
          if loss_weights is None:
              loss_weights_list = [1. for _ in range(len(self.outputs))]
          elif type(loss_weights) is dict:
              for name in loss_weights:
                  if name not in self.output_names:
                      raise Exception('Unknown entry in loss_weights '
                                      'dictionary: "' + name + '". '
                                      'Only expected the following keys: ' +
                                      str(self.output_names))
              loss_weights_list = []
              for name in self.output_names:
                  loss_weights_list.append(loss_weights.get(name, 1.))
          elif type(loss_weights) is list:
              if len(loss_weights) != len(self.outputs):
                  raise Exception('When passing a list as loss_weights, '
                                  'it should have one entry per model outputs. '
                                  'The model has ' + str(len(self.outputs)) +
                                  ' outputs, but you passed loss_weights=' +
                                  str(loss))
              loss_weights_list = loss_weights
          else:
              raise Exception('Could not interpret loss_weights argument: ' +
                              str(loss_weights))
  
          # prepare loss functions
          if type(loss) is dict:
              for name in loss:
                  if name not in self.output_names:
                      raise Exception('Unknown entry in loss '
                                      'dictionary: "' + name + '". '
                                      'Only expected the following keys: ' +
                                      str(self.output_names))
              loss_functions = []
              for name in self.output_names:
                  if name not in loss:
                      raise Exception('Output "' + name +
                                      '" missing from loss dictionary')
                  loss_functions.append(objectives.get(loss[name]))
          elif type(loss) is list:
              if len(loss) != len(self.outputs):
                  raise Exception('When passing a list as loss, '
                                  'it should have one entry per model outputs. '
                                  'The model has ' + str(len(self.outputs)) +
                                  ' outputs, but you passed loss=' +
                                  str(loss))
              loss_functions = [objectives.get(l) for l in loss]
          else:
              loss_function = objectives.get(loss)
              loss_functions = [loss_function for _ in range(len(self.outputs))]
          self.loss_functions = loss_functions
          weighted_losses = [weighted_objective(fn) for fn in loss_functions]
  
          # prepare output masks
          masks = self.compute_mask(self.inputs, mask=None)
          if masks is None:
              masks = [None for _ in self.outputs]
          if type(masks) is not list:
              masks = [masks]
  
          # prepare sample weights
          if type(sample_weight_mode) is dict:
              for name in sample_weight_mode:
                  if name not in self.output_names:
                      raise Exception('Unknown entry in '
                                      'sample_weight_mode dictionary: "' +
                                      name + '". '
                                      'Only expected the following keys: ' +
                                      str(self.output_names))
              sample_weights = []
              sample_weight_modes = []
              for name in self.output_names:
                  if name not in sample_weight_mode:
                      raise Exception('Output "' + name +
                                      '" missing from sample_weight_modes '
                                      'dictionary')
                  if sample_weight_mode.get(name) == 'temporal':
                      weight = K.placeholder(ndim=2, name=name + '_sample_weights')
                      sample_weight_modes.append('temporal')
                  else:
                      weight = K.placeholder(ndim=1, name=name + '_sample_weights')
                      sample_weight_modes.append(None)
                  sample_weights.append(weight)
          elif type(sample_weight_mode) is list:
              if len(sample_weight_mode) != len(self.outputs):
                  raise Exception('When passing a list as sample_weight_mode, ' +
                                  'it should have one entry per model outputs. '
                                  'The model has ' + str(len(self.outputs)) +
                                  ' outputs, but you passed sample_weight_mode=' +
                                  str(sample_weight_mode))
              sample_weights = []
              sample_weight_modes = []
              for mode, name in zip(sample_weight_mode, self.output_names):
                  if mode == 'temporal':
                      weight = K.placeholder(ndim=2, name=name + '_sample_weights')
                      sample_weight_modes.append('temporal')
                  else:
                      weight = K.placeholder(ndim=1, name=name + '_sample_weights')
                      sample_weight_modes.append(None)
                  sample_weights.append(weight)
          else:
              if sample_weight_mode == 'temporal':
                  sample_weights = [K.placeholder(ndim=2, name=name + '_sample_weights')
                                    for name in self.output_names]
                  sample_weight_modes = ['temporal' for name in self.output_names]
              else:
                  sample_weights = [K.placeholder(ndim=1, name=name + '_sample_weights')
                                    for name in self.output_names]
                  sample_weight_modes = [None for name in self.output_names]
          self.sample_weight_modes = sample_weight_modes
  
          # prepare targets of model
          self.targets = []
          for i in range(len(self.outputs)):
              shape = self.internal_output_shapes[i]
              name = self.output_names[i]
              self.targets.append(K.placeholder(ndim=len(shape), name=name + '_target'))
  
          # prepare metrics
          self.metrics_names = ['loss']
          self.metrics = []
  
          # compute total loss
          total_loss = None
          for i in range(len(self.outputs)):
              y_true = self.targets[i]
              y_pred = self.outputs[i]
              weighted_loss = weighted_losses[i]
              sample_weight = sample_weights[i]
              mask = masks[i]
              loss_weight = loss_weights_list[i]
              output_loss = weighted_loss(y_true, y_pred,
                                          sample_weight, mask)
              if len(self.outputs) > 1:
                  self.metrics.append(output_loss)
                  self.metrics_names.append(self.output_names[i] + '_loss')
              if total_loss is None:
                  total_loss = loss_weight * output_loss
              else:
                  total_loss += loss_weight * output_loss
  
          # add regularization penalties to the loss
          for r in self.regularizers:
              total_loss = r(total_loss)
  
          # list of same size as output_names.
          # contains tuples (metrics for output, names of metrics)
          nested_metrics = collect_metrics(metrics, self.output_names)
          for i in range(len(self.outputs)):
              y_true = self.targets[i]
              y_pred = self.outputs[i]
              output_metrics = nested_metrics[i]
  
              for metric in output_metrics:
                  if metric == 'accuracy' or metric == 'acc':
                      # custom handling of accuracy (because of class mode duality)
                      output_shape = self.internal_output_shapes[i]
                      if output_shape[-1] == 1:
                          # case: binary accuracy
                          self.metrics.append(metrics_module.binary_accuracy(y_true, y_pred))
                      elif self.loss_functions[i] == objectives.sparse_categorical_crossentropy:
                          # case: categorical accuracy with sparse targets
                          self.metrics.append(
                              metrics_module.sparse_categorical_accuracy(y_true, y_pred))
                      else:
                          # case: categorical accuracy with dense targets
                          self.metrics.append(metrics_module.categorical_accuracy(y_true, y_pred))
                      if len(self.output_names) == 1:
                          self.metrics_names.append('acc')
                      else:
                          self.metrics_names.append(self.output_layers[i].name + '_acc')
                  else:
                      metric_fn = metrics_module.get(metric)
                      self.metrics.append(metric_fn(y_true, y_pred))
                      if len(self.output_names) == 1:
                          self.metrics_names.append(metric_fn.__name__)
                      else:
                          self.metrics_names.append(self.output_layers[i].name + '_' + metric_fn.__name__)
  
          # prepare gradient updates and state updates
          self.optimizer = optimizers.get(optimizer)
          self.total_loss = total_loss
          self.sample_weights = sample_weights
  
          # functions for train, test and predict will
          # be compiled lazily when required.
          # This saves time when the user is not using all functions.
          self._function_kwargs = kwargs
  
          self.train_function = None
          self.test_function = None
          self.predict_function = None
  
      def _make_train_function(self):
          if not hasattr(self, 'train_function'):
              raise Exception('You must compile your model before using it.')
          if self.train_function is None:
              if self.uses_learning_phase:
                  inputs = self.inputs + self.targets + self.sample_weights + [K.learning_phase()]
              else:
                  inputs = self.inputs + self.targets + self.sample_weights
  
              # get trainable weights
              trainable_weights = []
              for layer in self.layers:
                  trainable_weights += collect_trainable_weights(layer)
  
              training_updates = self.optimizer.get_updates(trainable_weights, self.constraints, self.total_loss)
              updates = self.updates + training_updates
  
              # returns loss and metrics. Updates weights at each call.
              self.train_function = K.function(inputs,
                                               [self.total_loss] + self.metrics,
                                               updates=updates,
                                               **self._function_kwargs)
  
      def _make_test_function(self):
          if not hasattr(self, 'test_function'):
              raise Exception('You must compile your model before using it.')
          if self.test_function is None:
              if self.uses_learning_phase:
                  inputs = self.inputs + self.targets + self.sample_weights + [K.learning_phase()]
              else:
                  inputs = self.inputs + self.targets + self.sample_weights
              # return loss and metrics, no gradient updates.
              # Does update the network states.
              self.test_function = K.function(inputs,
                                              [self.total_loss] + self.metrics,
                                              updates=self.state_updates,
                                              **self._function_kwargs)
  
      def _make_predict_function(self):
          if not hasattr(self, 'predict_function'):
              self.predict_function = None
          if self.predict_function is None:
              if self.uses_learning_phase:
                  inputs = self.inputs + [K.learning_phase()]
              else:
                  inputs = self.inputs
              # returns network outputs. Does not update weights.
              # Does update the network states.
              kwargs = getattr(self, '_function_kwargs', {})
              self.predict_function = K.function(inputs,
                                                 self.outputs,
                                                 updates=self.state_updates,
                                                 **kwargs)
  
      def _fit_loop(self, f, ins, out_labels=[], batch_size=32,
                    nb_epoch=100, verbose=1, callbacks=[],
                    val_f=None, val_ins=None, shuffle=True,
                    callback_metrics=[]):
          '''Abstract fit function for f(ins).
          Assume that f returns a list, labeled by out_labels.
  
          # Arguments
              f: Keras function returning a list of tensors
              ins: list of tensors to be fed to `f`
              out_labels: list of strings, display names of
                  the outputs of `f`
              batch_size: integer batch size
              nb_epoch: number of times to iterate over the data
              verbose: verbosity mode, 0, 1 or 2
              callbacks: list of callbacks to be called during training
              val_f: Keras function to call for validation
              val_ins: list of tensors to be fed to `val_f`
              shuffle: whether to shuffle the data at the beginning of each epoch
              callback_metrics: list of strings, the display names of the metrics
                  passed to the callbacks. They should be the
                  concatenation of list the display names of the outputs of
                   `f` and the list of display names of the outputs of `f_val`.
  
          # Returns
              `History` object.
          '''
          do_validation = False
          if val_f and val_ins:
              do_validation = True
              if verbose:
                  print('Train on %d samples, validate on %d samples' %
                        (len(ins[0]), len(val_ins[0])))
  
          nb_train_sample = len(ins[0])
          index_array = np.arange(nb_train_sample)
  
          self.history = cbks.History()
          callbacks = [cbks.BaseLogger()] + callbacks + [self.history]
          if verbose:
              callbacks += [cbks.ProgbarLogger()]
          callbacks = cbks.CallbackList(callbacks)
  
          # it's possible to callback a different model than self
          # (used by Sequential models)
          if hasattr(self, 'callback_model') and self.callback_model:
              callback_model = self.callback_model
          else:
              callback_model = self
  
          callbacks._set_model(callback_model)
          callbacks._set_params({
              'batch_size': batch_size,
              'nb_epoch': nb_epoch,
              'nb_sample': nb_train_sample,
              'verbose': verbose,
              'do_validation': do_validation,
              'metrics': callback_metrics,
          })
          callbacks.on_train_begin()
          callback_model.stop_training = False
          self.validation_data = val_ins
  
          for epoch in range(nb_epoch):
              callbacks.on_epoch_begin(epoch)
              if shuffle == 'batch':
                  index_array = batch_shuffle(index_array, batch_size)
              elif shuffle:
                  np.random.shuffle(index_array)
  
              batches = make_batches(nb_train_sample, batch_size)
              for batch_index, (batch_start, batch_end) in enumerate(batches):
                  batch_ids = index_array[batch_start:batch_end]
                  try:
                      if type(ins[-1]) is float:
                          # do not slice the training phase flag
                          ins_batch = slice_X(ins[:-1], batch_ids) + [ins[-1]]
                      else:
                          ins_batch = slice_X(ins, batch_ids)
                  except TypeError:
                      raise Exception('TypeError while preparing batch. '
                                      'If using HDF5 input data, '
                                      'pass shuffle="batch".')
                  batch_logs = {}
                  batch_logs['batch'] = batch_index
                  batch_logs['size'] = len(batch_ids)
                  callbacks.on_batch_begin(batch_index, batch_logs)
                  outs = f(ins_batch)
                  if type(outs) != list:
                      outs = [outs]
                  for l, o in zip(out_labels, outs):
                      batch_logs[l] = o
  
                  callbacks.on_batch_end(batch_index, batch_logs)
  
                  epoch_logs = {}
                  if batch_index == len(batches) - 1:  # last batch
                      # validation
                      if do_validation:
                          # replace with self._evaluate
                          val_outs = self._test_loop(val_f, val_ins,
                                                     batch_size=batch_size,
                                                     verbose=0)
                          if type(val_outs) != list:
                              val_outs = [val_outs]
                          # same labels assumed
                          for l, o in zip(out_labels, val_outs):
                              epoch_logs['val_' + l] = o
              callbacks.on_epoch_end(epoch, epoch_logs)
              if callback_model.stop_training:
                  break
          callbacks.on_train_end()
          return self.history
  
      def _predict_loop(self, f, ins, batch_size=32, verbose=0):
          '''Abstract method to loop over some data in batches.
  
          # Arguments
              f: Keras function returning a list of tensors.
              ins: list of tensors to be fed to `f`.
              batch_size: integer batch size.
              verbose: verbosity mode.
  
          # Returns
              Array of predictions (if the model has a single output)
              or list of arrays of predictions
              (if the model has multiple outputs).
          '''
          nb_sample = len(ins[0])
          outs = []
          if verbose == 1:
              progbar = Progbar(target=nb_sample)
          batches = make_batches(nb_sample, batch_size)
          index_array = np.arange(nb_sample)
          for batch_index, (batch_start, batch_end) in enumerate(batches):
              batch_ids = index_array[batch_start:batch_end]
              if type(ins[-1]) is float:
                  # do not slice the training phase flag
                  ins_batch = slice_X(ins[:-1], batch_ids) + [ins[-1]]
              else:
                  ins_batch = slice_X(ins, batch_ids)
  
              batch_outs = f(ins_batch)
              if type(batch_outs) != list:
                  batch_outs = [batch_outs]
              if batch_index == 0:
                  for batch_out in batch_outs:
                      shape = (nb_sample,) + batch_out.shape[1:]
                      outs.append(np.zeros(shape))
  
              for i, batch_out in enumerate(batch_outs):
                  outs[i][batch_start:batch_end] = batch_out
              if verbose == 1:
                  progbar.update(batch_end)
          if len(outs) == 1:
              return outs[0]
          return outs
  
      def _test_loop(self, f, ins, batch_size=32, verbose=0):
          '''Abstract method to loop over some data in batches.
  
          # Arguments
              f: Keras function returning a list of tensors.
              ins: list of tensors to be fed to `f`.
              batch_size: integer batch size.
              verbose: verbosity mode.
  
          # Returns
              Scalar loss (if the model has a single output and no metrics)
              or list of scalars (if the model has multiple outputs
              and/or metrics). The attribute `model.metrics_names` will give you
              the display labels for the scalar outputs.
          '''
          nb_sample = len(ins[0])
          outs = []
          if verbose == 1:
              progbar = Progbar(target=nb_sample)
          batches = make_batches(nb_sample, batch_size)
          index_array = np.arange(nb_sample)
          for batch_index, (batch_start, batch_end) in enumerate(batches):
              batch_ids = index_array[batch_start:batch_end]
              if type(ins[-1]) is float:
                  # do not slice the training phase flag
                  ins_batch = slice_X(ins[:-1], batch_ids) + [ins[-1]]
              else:
                  ins_batch = slice_X(ins, batch_ids)
  
              batch_outs = f(ins_batch)
              if type(batch_outs) == list:
                  if batch_index == 0:
                      for batch_out in enumerate(batch_outs):
                          outs.append(0.)
                  for i, batch_out in enumerate(batch_outs):
                      outs[i] += batch_out * len(batch_ids)
              else:
                  if batch_index == 0:
                      outs.append(0.)
                  outs[0] += batch_outs * len(batch_ids)
  
              if verbose == 1:
                  progbar.update(batch_end)
          for i, out in enumerate(outs):
              outs[i] /= nb_sample
          if len(outs) == 1:
              return outs[0]
          return outs
  
      def _standardize_user_data(self, x, y,
                                 sample_weight=None, class_weight=None,
                                 check_batch_dim=True, batch_size=None):
          if not hasattr(self, 'optimizer'):
              raise Exception('You must compile a model before training/testing.'
                              ' Use `model.compile(optimizer, loss)`.')
  
          # if using sparse cce replace last dim of output shapes with 1
          output_shapes = self.internal_output_shapes
          if self.loss == "sparse_categorical_crossentropy":
              output_shapes = [s[:-1] + (1,) for s in output_shapes]
          x = standardize_input_data(x, self.input_names,
                                     self.internal_input_shapes,
                                     check_batch_dim=False,
                                     exception_prefix='model input')
          y = standardize_input_data(y, self.output_names,
                                     output_shapes,
                                     check_batch_dim=False,
                                     exception_prefix='model target')
          sample_weights = standardize_sample_weights(sample_weight,
                                                      self.output_names)
          class_weights = standardize_class_weights(class_weight,
                                                    self.output_names)
          sample_weights = [standardize_weights(ref, sw, cw, mode)
                            for (ref, sw, cw, mode)
                            in zip(y, sample_weights, class_weights, self.sample_weight_modes)]
          check_array_lengths(x, y, sample_weights)
          check_loss_and_target_compatibility(y, self.loss_functions, self.internal_output_shapes)
          if self.stateful and batch_size:
              if x[0].shape[0] % batch_size != 0:
                  raise Exception('In a stateful network, '
                                  'you should only pass inputs with '
                                  'a number of samples that can be '
                                  'divided by the batch size. Found: ' +
                                  str(x[0].shape[0]) + ' samples')
          return x, y, sample_weights
  
      def fit(self, x, y, batch_size=32, nb_epoch=10, verbose=1, callbacks=[],
              validation_split=0., validation_data=None, shuffle=True,
              class_weight=None, sample_weight=None):
          '''Trains the model for a fixed number of epochs (iterations on a dataset).
  
          # Arguments
              x: Numpy array of training data,
                  or list of Numpy arrays if the model has multiple inputs.
                  If all inputs in the model are named, you can also pass a dictionary
                  mapping input names to Numpy arrays.
              y: Numpy array of target data,
                  or list of Numpy arrays if the model has multiple outputs.
                  If all outputs in the model are named, you can also pass a dictionary
                  mapping output names to Numpy arrays.
              batch_size: integer. Number of samples per gradient update.
              nb_epoch: integer, the number of times to iterate over the training data arrays.
              verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, 2 = one log line per epoch.
              callbacks: list of callbacks to be called during training.
                  See [callbacks](/callbacks).
              validation_split: float between 0 and 1:
                  fraction of the training data to be used as validation data.
                  The model will set apart this fraction of the training data,
                  will not train on it, and will evaluate the loss and any model metrics
                  on this data at the end of each epoch.
              validation_data: data on which to evaluate the loss and any model metrics
                  at the end of each epoch. The model will not be trained on this data.
                  This could be a tuple (x_val, y_val) or a tuple (val_x, val_y, val_sample_weights).
              shuffle: boolean, whether to shuffle the training data before each epoch.
              class_weight: optional dictionary mapping class indices (integers) to
                  a weight (float) to apply to the model's loss for the samples
                  from this class during training.
                  This can be useful to tell the model to "pay more attention" to
                  samples from an under-represented class.
              sample_weight: optional array of the same length as x, containing
                  weights to apply to the model's loss for each sample.
                  In the case of temporal data, you can pass a 2D array
                  with shape (samples, sequence_length),
                  to apply a different weight to every timestep of every sample.
                  In this case you should make sure to specify sample_weight_mode="temporal" in compile().
  
  
          # Returns
              A `History` instance. Its `history` attribute contains
              all information collected during training.
          '''
          # validate user data
          x, y, sample_weights = self._standardize_user_data(x, y,
                                                             sample_weight=sample_weight,
                                                             class_weight=class_weight,
                                                             check_batch_dim=False,
                                                             batch_size=batch_size)
          # prepare validation data
          if validation_data:
              do_validation = True
              if len(validation_data) == 2:
                  val_x, val_y = validation_data
                  val_sample_weight = None
              elif len(validation_data) == 3:
                  val_x, val_y, val_sample_weight = validation_data
              else:
                  raise
              val_x, val_y, val_sample_weights = self._standardize_user_data(val_x, val_y,
                                                                             sample_weight=val_sample_weight,
                                                                             check_batch_dim=False,
                                                                             batch_size=batch_size)
              self._make_test_function()
              val_f = self.test_function
              if self.uses_learning_phase:
                  val_ins = val_x + val_y + val_sample_weights + [0.]
              else:
                  val_ins = val_x + val_y + val_sample_weights
  
          elif validation_split and 0. < validation_split < 1.:
              do_validation = True
              split_at = int(len(x[0]) * (1. - validation_split))
              x, val_x = (slice_X(x, 0, split_at), slice_X(x, split_at))
              y, val_y = (slice_X(y, 0, split_at), slice_X(y, split_at))
              sample_weights, val_sample_weights = (slice_X(sample_weights, 0, split_at), slice_X(sample_weights, split_at))
              self._make_test_function()
              val_f = self.test_function
              if self.uses_learning_phase:
                  val_ins = val_x + val_y + val_sample_weights + [0.]
              else:
                  val_ins = val_x + val_y + val_sample_weights
          else:
              do_validation = False
              val_f = None
              val_ins = None
  
          # prepare input arrays and training function
          if self.uses_learning_phase:
              ins = x + y + sample_weights + [1.]
          else:
              ins = x + y + sample_weights
          self._make_train_function()
          f = self.train_function
  
          # prepare display labels
          out_labels = self.metrics_names
  
          # rename duplicated metrics name
          # (can happen with an output layer shared among multiple dataflows)
          deduped_out_labels = []
          for i, label in enumerate(out_labels):
              new_label = label
              if out_labels.count(label) > 1:
                  dup_idx = out_labels[:i].count(label)
                  new_label += '_' + str(dup_idx + 1)
              deduped_out_labels.append(new_label)
          out_labels = deduped_out_labels
  
          if do_validation:
              callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels]
          else:
              callback_metrics = copy.copy(out_labels)
  
          # delegate logic to _fit_loop
          return self._fit_loop(f, ins, out_labels=out_labels,
                                batch_size=batch_size, nb_epoch=nb_epoch,
                                verbose=verbose, callbacks=callbacks,
                                val_f=val_f, val_ins=val_ins, shuffle=shuffle,
                                callback_metrics=callback_metrics)
  
      def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
          '''Returns the loss value and metrics values for the model
          in test mode. Computation in done in batches.
  
          # Arguments
              x: Numpy array of test data,
                  or list of Numpy arrays if the model has multiple inputs.
                  If all inputs in the model are named, you can also pass a dictionary
                  mapping input names to Numpy arrays.
              y: Numpy array of target data,
                  or list of Numpy arrays if the model has multiple outputs.
                  If all outputs in the model are named, you can also pass a dictionary
                  mapping output names to Numpy arrays.
              batch_size: integer. Number of samples per gradient update.
  
          # Returns
              Scalar test loss (if the model has a single output and no metrics)
              or list of scalars (if the model has multiple outputs
              and/or metrics). The attribute `model.metrics_names` will give you
              the display labels for the scalar outputs.
          '''
          # validate user data
          x, y, sample_weights = self._standardize_user_data(x, y,
                                                             sample_weight=sample_weight,
                                                             check_batch_dim=False,
                                                             batch_size=batch_size)
          # prepare inputs, delegate logic to _test_loop
          if self.uses_learning_phase:
              ins = x + y + sample_weights + [0.]
          else:
              ins = x + y + sample_weights
          self._make_test_function()
          f = self.test_function
          return self._test_loop(f, ins,
                                 batch_size=batch_size,
                                 verbose=verbose)
  
      def predict(self, x, batch_size=32, verbose=0):
          '''Generates output predictions for the input samples,
          processing the samples in a batched way.
  
          # Arguments
              x: the input data, as a Numpy array
                  (or list of Numpy arrays if the model has multiple outputs).
              batch_size: integer.
              verbose: verbosity mode, 0 or 1.
  
          # Returns
              A Numpy array of predictions.
          '''
          # validate user data
          x = standardize_input_data(x, self.input_names,
                                     self.internal_input_shapes,
                                     check_batch_dim=False)
          if self.stateful:
              if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
                  raise Exception('In a stateful network, '
                                  'you should only pass inputs with '
                                  'a number of samples that can be '
                                  'divided by the batch size. Found: ' +
                                  str(x[0].shape[0]) + ' samples. '
                                  'Batch size: ' + str(batch_size) + '.')
  
          # prepare inputs, delegate logic to _predict_loop
          if self.uses_learning_phase:
              ins = x + [0.]
          else:
              ins = x
          self._make_predict_function()
          f = self.predict_function
          return self._predict_loop(f, ins,
                                    batch_size=batch_size, verbose=verbose)
  
      def train_on_batch(self, x, y,
                         sample_weight=None, class_weight=None):
          '''Runs a single gradient update on a single batch of data.
  
          # Arguments
              x: Numpy array of training data,
                  or list of Numpy arrays if the model has multiple inputs.
                  If all inputs in the model are named, you can also pass a dictionary
                  mapping input names to Numpy arrays.
              y: Numpy array of target data,
                  or list of Numpy arrays if the model has multiple outputs.
                  If all outputs in the model are named, you can also pass a dictionary
                  mapping output names to Numpy arrays.
              sample_weight: optional array of the same length as x, containing
                  weights to apply to the model's loss for each sample.
                  In the case of temporal data, you can pass a 2D array
                  with shape (samples, sequence_length),
                  to apply a different weight to every timestep of every sample.
                  In this case you should make sure to specify sample_weight_mode="temporal" in compile().
              class_weight: optional dictionary mapping class indices (integers) to
                  a weight (float) to apply to the model's loss for the samples
                  from this class during training.
                  This can be useful to tell the model to "pay more attention" to
                  samples from an under-represented class.
  
          # Returns
              Scalar training loss (if the model has a single output and no metrics)
              or list of scalars (if the model has multiple outputs
              and/or metrics). The attribute `model.metrics_names` will give you
              the display labels for the scalar outputs.
          '''
          x, y, sample_weights = self._standardize_user_data(x, y,
                                                             sample_weight=sample_weight,
                                                             class_weight=class_weight,
                                                             check_batch_dim=True)
          if self.uses_learning_phase:
              ins = x + y + sample_weights + [1.]
          else:
              ins = x + y + sample_weights
          self._make_train_function()
          outputs = self.train_function(ins)
          if len(outputs) == 1:
              return outputs[0]
          return outputs
  
      def test_on_batch(self, x, y, sample_weight=None):
          '''Test the model on a single batch of samples.
  
          # Arguments
              x: Numpy array of test data,
                  or list of Numpy arrays if the model has multiple inputs.
                  If all inputs in the model are named, you can also pass a dictionary
                  mapping input names to Numpy arrays.
              y: Numpy array of target data,
                  or list of Numpy arrays if the model has multiple outputs.
                  If all outputs in the model are named, you can also pass a dictionary
                  mapping output names to Numpy arrays.
              sample_weight: optional array of the same length as x, containing
                  weights to apply to the model's loss for each sample.
                  In the case of temporal data, you can pass a 2D array
                  with shape (samples, sequence_length),
                  to apply a different weight to every timestep of every sample.
                  In this case you should make sure to specify sample_weight_mode="temporal" in compile().
  
          # Returns
              Scalar test loss (if the model has a single output and no metrics)
              or list of scalars (if the model has multiple outputs
              and/or metrics). The attribute `model.metrics_names` will give you
              the display labels for the scalar outputs.
          '''
          x, y, sample_weights = self._standardize_user_data(x, y,
                                                             sample_weight=sample_weight,
                                                             check_batch_dim=True)
          if self.uses_learning_phase:
              ins = x + y + sample_weights + [0.]
          else:
              ins = x + y + sample_weights
          self._make_test_function()
          outputs = self.test_function(ins)
          if len(outputs) == 1:
              return outputs[0]
          return outputs
  
      def predict_on_batch(self, x):
          '''Returns predictions for a single batch of samples.
          '''
          x = standardize_input_data(x, self.input_names,
                                     self.internal_input_shapes)
          if self.uses_learning_phase:
              ins = x + [0.]
          else:
              ins = x
          self._make_predict_function()
          outputs = self.predict_function(ins)
          if len(outputs) == 1:
              return outputs[0]
          return outputs
  
      def fit_generator(self, generator, samples_per_epoch, nb_epoch,
                        verbose=1, callbacks=[],
                        validation_data=None, nb_val_samples=None,
                        class_weight={}, max_q_size=10):
          '''Fits the model on data generated batch-by-batch by
          a Python generator.
          The generator is run in parallel to the model, for efficiency.
          For instance, this allows you to do real-time data augmentation
          on images on CPU in parallel to training your model on GPU.
  
          # Arguments
              generator: a generator.
                  The output of the generator must be either
                  - a tuple (inputs, targets)
                  - a tuple (inputs, targets, sample_weights).
                  All arrays should contain the same number of samples.
                  The generator is expected to loop over its data
                  indefinitely. An epoch finishes when `samples_per_epoch`
                  samples have been seen by the model.
              samples_per_epoch: integer, number of samples to process before
                  going to the next epoch.
              nb_epoch: integer, total number of iterations on the data.
              verbose: verbosity mode, 0, 1, or 2.
              callbacks: list of callbacks to be called during training.
              validation_data: this can be either
                  - a generator for the validation data
                  - a tuple (inputs, targets)
                  - a tuple (inputs, targets, sample_weights).
              nb_val_samples: only relevant if `validation_data` is a generator.
                  number of samples to use from validation generator
                  at the end of every epoch.
              class_weight: dictionary mapping class indices to a weight
                  for the class.
              max_q_size: maximum size for the generator queue
  
          # Returns
              A `History` object.
  
          # Example
  
          ```python
              def generate_arrays_from_file(path):
                  while 1:
                      f = open(path)
                      for line in f:
                          # create numpy arrays of input data
                          # and labels, from each line in the file
                          x1, x2, y = process_line(line)
                          yield ({'input_1': x1, 'input_2': x2}, {'output': y})
                      f.close()
  
              model.fit_generator(generate_arrays_from_file('/my_file.txt'),
                                  samples_per_epoch=10000, nb_epoch=10)
          ```
          '''
          wait_time = 0.01  # in seconds
          epoch = 0
  
          do_validation = bool(validation_data)
          self._make_train_function()
          if do_validation:
              self._make_test_function()
  
          # python 2 has 'next', 3 has '__next__'
          # avoid any explicit version checks
          val_gen = (hasattr(validation_data, 'next') or
                     hasattr(validation_data, '__next__'))
          if val_gen and not nb_val_samples:
              raise Exception('When using a generator for validation data, '
                              'you must specify a value for "nb_val_samples".')
  
          out_labels = self.metrics_names
          callback_metrics = out_labels + ['val_' + n for n in out_labels]
  
          # prepare callbacks
          self.history = cbks.History()
          callbacks = [cbks.BaseLogger()] + callbacks + [self.history]
          if verbose:
              callbacks += [cbks.ProgbarLogger()]
          callbacks = cbks.CallbackList(callbacks)
  
          # it's possible to callback a different model than self:
          if hasattr(self, 'callback_model') and self.callback_model:
              callback_model = self.callback_model
          else:
              callback_model = self
          callbacks._set_model(callback_model)
          callbacks._set_params({
              'nb_epoch': nb_epoch,
              'nb_sample': samples_per_epoch,
              'verbose': verbose,
              'do_validation': do_validation,
              'metrics': callback_metrics,
          })
          callbacks.on_train_begin()
  
          if do_validation and not val_gen:
              if len(validation_data) == 2:
                  val_x, val_y = validation_data
                  val_sample_weight = None
              elif len(validation_data) == 3:
                  val_x, val_y, val_sample_weight = validation_data
              else:
                  raise Exception('validation_data should be a tuple '
                                  '(val_x, val_y, val_sample_weight) '
                                  'or (val_x, val_y). Found: ' + str(validation_data))
              val_x, val_y, val_sample_weights = self._standardize_user_data(val_x, val_y, val_sample_weight)
              self.validation_data = val_x + [val_y, val_sample_weights]
          else:
              self.validation_data = None
  
          # start generator thread storing batches into a queue
          data_gen_queue, _stop = generator_queue(generator, max_q_size=max_q_size)
  
          callback_model.stop_training = False
          while epoch < nb_epoch:
              callbacks.on_epoch_begin(epoch)
              samples_seen = 0
              batch_index = 0
              while samples_seen < samples_per_epoch:
                  generator_output = None
                  while not _stop.is_set():
                      if not data_gen_queue.empty():
                          generator_output = data_gen_queue.get()
                          break
                      else:
                          time.sleep(wait_time)
  
                  if not hasattr(generator_output, '__len__'):
                      _stop.set()
                      raise Exception('output of generator should be a tuple '
                                      '(x, y, sample_weight) '
                                      'or (x, y). Found: ' + str(generator_output))
                  if len(generator_output) == 2:
                      x, y = generator_output
                      sample_weight = None
                  elif len(generator_output) == 3:
                      x, y, sample_weight = generator_output
                  else:
                      _stop.set()
                      raise Exception('output of generator should be a tuple '
                                      '(x, y, sample_weight) '
                                      'or (x, y). Found: ' + str(generator_output))
                  # build batch logs
                  batch_logs = {}
                  if type(x) is list:
                      batch_size = len(x[0])
                  elif type(x) is dict:
                      batch_size = len(list(x.values())[0])
                  else:
                      batch_size = len(x)
                  batch_logs['batch'] = batch_index
                  batch_logs['size'] = batch_size
                  callbacks.on_batch_begin(batch_index, batch_logs)
  
                  try:
                      outs = self.train_on_batch(x, y,
                                                 sample_weight=sample_weight,
                                                 class_weight=class_weight)
                  except Exception as e:
                      _stop.set()
                      raise
  
                  if type(outs) != list:
                      outs = [outs]
                  for l, o in zip(out_labels, outs):
                      batch_logs[l] = o
  
                  callbacks.on_batch_end(batch_index, batch_logs)
  
                  # construct epoch logs
                  epoch_logs = {}
                  batch_index += 1
                  samples_seen += batch_size
  
                  # epoch finished
                  if samples_seen > samples_per_epoch:
                      warnings.warn('Epoch comprised more than '
                                    '`samples_per_epoch` samples, '
                                    'which might affect learning results. '
                                    'Set `samples_per_epoch` correctly '
                                    'to avoid this warning.')
                  if samples_seen >= samples_per_epoch and do_validation:
                      if val_gen:
                          val_outs = self.evaluate_generator(validation_data,
                                                             nb_val_samples,
                                                             max_q_size=max_q_size)
                      else:
                          # no need for try/except because
                          # data has already been validated
                          val_outs = self.evaluate(val_x, val_y,
                                                   sample_weight=val_sample_weights,
                                                   verbose=0)
                      if type(val_outs) is not list:
                          val_outs = [val_outs]
                      # same labels assumed
                      for l, o in zip(out_labels, val_outs):
                          epoch_logs['val_' + l] = o
  
              callbacks.on_epoch_end(epoch, epoch_logs)
              epoch += 1
              if callback_model.stop_training:
                  break
  
          _stop.set()
          callbacks.on_train_end()
          return self.history
  
      def evaluate_generator(self, generator, val_samples, max_q_size=10):
          '''Evaluates the model on a data generator. The generator should
          return the same kind of data as accepted by `test_on_batch`.
  
          Arguments:
              generator:
                  generator yielding tuples (inputs, targets)
                  or (inputs, targets, sample_weights)
              val_samples:
                  total number of samples to generate from `generator`
                  before returning.
              max_q_size: maximum size for the generator queue
  
          # Returns
              Scalar test loss (if the model has a single output and no metrics)
              or list of scalars (if the model has multiple outputs
              and/or metrics). The attribute `model.metrics_names` will give you
              the display labels for the scalar outputs.
          '''
          self._make_test_function()
  
          processed_samples = 0
          wait_time = 0.01
          all_outs = []
          weights = []
          data_gen_queue, _stop = generator_queue(generator, max_q_size=max_q_size)
  
          while processed_samples < val_samples:
              generator_output = None
              while not _stop.is_set():
                  if not data_gen_queue.empty():
                      generator_output = data_gen_queue.get()
                      break
                  else:
                      time.sleep(wait_time)
  
              if not hasattr(generator_output, '__len__'):
                  _stop.set()
                  raise Exception('output of generator should be a tuple '
                                  '(x, y, sample_weight) '
                                  'or (x, y). Found: ' + str(generator_output))
              if len(generator_output) == 2:
                  x, y = generator_output
                  sample_weight = None
              elif len(generator_output) == 3:
                  x, y, sample_weight = generator_output
              else:
                  _stop.set()
                  raise Exception('output of generator should be a tuple '
                                  '(x, y, sample_weight) '
                                  'or (x, y). Found: ' + str(generator_output))
              try:
                  outs = self.test_on_batch(x, y, sample_weight=sample_weight)
              except Exception as e:
                  _stop.set()
                  raise
  
              if type(x) is list:
                  nb_samples = len(x[0])
              elif type(x) is dict:
                  nb_samples = len(list(x.values())[0])
              else:
                  nb_samples = len(x)
              all_outs.append(outs)
  
              processed_samples += nb_samples
              weights.append(nb_samples)
  
          _stop.set()
          if type(outs) is not list:
              return np.average(np.asarray(all_outs),
                                weights=weights)
          else:
              averages = []
              for i in range(len(outs)):
                  averages.append(np.average([out[i] for out in all_outs],
                                  weights=weights))
              return averages
  
      def predict_generator(self, generator, val_samples, max_q_size=10):
          '''Generates predictions for the input samples from a data generator.
          The generator should return the same kind of data as accepted by
          `predict_on_batch`.
  
          # Arguments
              generator: generator yielding batches of input samples.
              val_samples: total number of samples to generate from `generator`
                  before returning.
              max_q_size: maximum size for the generator queue
  
          # Returns
              Numpy array(s) of predictions.
          '''
          self._make_predict_function()
  
          processed_samples = 0
          wait_time = 0.01
          all_outs = []
          data_gen_queue, _stop = generator_queue(generator, max_q_size=max_q_size)
  
          while processed_samples < val_samples:
              generator_output = None
              while not _stop.is_set():
                  if not data_gen_queue.empty():
                      generator_output = data_gen_queue.get()
                      break
                  else:
                      time.sleep(wait_time)
  
              if isinstance(generator_output, tuple):
                  if len(generator_output) == 2:
                      x, y = generator_output
                      sample_weight = None
                  elif len(generator_output) == 3:
                      x, y, sample_weight = generator_output
                  else:
                      _stop.set()
                      raise Exception('output of generator should be a tuple '
                                      '(x, y, sample_weight) '
                                      'or (x, y). Found: ' + str(generator_output))
              else:
                  x = generator_output
  
              try:
                  outs = self.predict_on_batch(x)
              except Exception as e:
                  _stop.set()
                  raise
  
              if type(x) is list:
                  nb_samples = len(x[0])
              elif type(x) is dict:
                  nb_samples = len(list(x.values())[0])
              else:
                  nb_samples = len(x)
  
              if type(outs) != list:
                  outs = [outs]
  
              if len(all_outs) == 0:
                  for out in outs:
                      shape = (val_samples,) + out.shape[1:]
                      all_outs.append(np.zeros(shape))
  
              for i, out in enumerate(outs):
                  all_outs[i][processed_samples:(processed_samples + nb_samples)] = out
  
              processed_samples += nb_samples
  
          _stop.set()
          if len(all_outs) == 1:
              return all_outs[0]
          return all_outs
  