text: # -*- coding: utf-8 -*-
  from __future__ import print_function
  from __future__ import absolute_import
  
  import warnings
  import copy
  import numpy as np
  import six
  
  from keras.utils import Sequence
  from keras.utils import GeneratorEnqueuer
  from keras.utils import OrderedEnqueuer
  
  try:
      import queue
  except ImportError:
      import Queue as queue
  
  from .topology import Container
  from .. import backend as K
  from .. import optimizers
  from .. import losses
  from .. import metrics as metrics_module
  from ..utils.generic_utils import Progbar
  from .. import callbacks as cbks
  from ..legacy import interfaces
  
  
  def _standardize_input_data(data, names, shapes=None,
                              check_batch_axis=True,
                              exception_prefix=''):
      """Normalizes inputs and targets provided by users.
  
      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.
  
      # Arguments
          data: User-provided input data (polymorphic).
          names: List of expected array names.
          shapes: Optional list of expected array shapes.
          check_batch_axis: Boolean; whether to check that
              the batch axis of the arrays matches the expected
              value found in `shapes`.
          exception_prefix: String prefix used for exception formatting.
  
      # Returns
          List of standardized input arrays (one array per model input).
  
      # Raises
          ValueError: in case of improperly formatted user-provided data.
      """
      if not names:
          if data:
              raise ValueError('Error when checking model ' +
                               exception_prefix + ': '
                               'expected no data, but got:', data)
          return []
      if data is None:
          return [None for _ in range(len(names))]
      if isinstance(data, dict):
          arrays = []
          for name in names:
              if name not in data:
                  raise ValueError('No data provided for "' +
                                   name + '". Need data for each key in: ' +
                                   str(names))
              arrays.append(data[name])
      elif isinstance(data, list):
          if len(data) != len(names):
              if data and hasattr(data[0], 'shape'):
                  raise ValueError('Error when checking model ' +
                                   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)) +
                                   ' array(s), 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 ValueError(
                          'Error when checking model ' +
                          exception_prefix +
                          ': you are passing a list as '
                          'input to your model, '
                          'but the model expects '
                          '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 TypeError('Error when checking model ' +
                              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 ValueError('The model expects ' + str(len(names)) + ' ' +
                               exception_prefix +
                               ' 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)):
              if shapes[i] is None:
                  continue
              array = arrays[i]
              if len(array.shape) != len(shapes[i]):
                  raise ValueError('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_axis:
                      # skip the first axis
                      continue
                  if ref_dim:
                      if ref_dim != dim:
                          raise ValueError(
                              '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):
      """Maps `sample_weight` or `class_weight` to model outputs.
  
      # Arguments
          x_weight: User-provided `sample_weight` or `class_weight` argument.
          output_names: List of output names (strings) in the model.
          weight_type: A string used purely for exception printing.
  
      # Returns
          A list of `sample_weight` or `class_weight` where there are exactly
              one element per model output.
  
      # Raises
          ValueError: In case of invalid user-provided argument.
      """
      if x_weight is None or len(x_weight) == 0:
          return [None for _ in output_names]
      if len(output_names) == 1:
          if isinstance(x_weight, list) and len(x_weight) == 1:
              return x_weight
          if isinstance(x_weight, dict) and output_names[0] in x_weight:
              return [x_weight[output_names[0]]]
          else:
              return [x_weight]
      if isinstance(x_weight, list):
          if len(x_weight) != len(output_names):
              raise ValueError('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 isinstance(x_weight, dict):
          x_weights = []
          for name in output_names:
              x_weights.append(x_weight.get(name))
          return x_weights
      else:
          raise TypeError('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(inputs, targets, weights=None):
      """Does user input validation for numpy arrays.
  
      # Arguments
          inputs: list of Numpy arrays of inputs.
          targets: list of Numpy arrays of targets.
          weights: list of Numpy arrays of sample weights.
  
      # Raises
          ValueError: in case of incorrectly formatted data.
      """
      def set_of_lengths(x):
          # return a set with the variation between
          # different shapes, with None => 0
          if x is None:
              return {0}
          else:
              return set([0 if y is None else y.shape[0] for y in x])
  
      set_x = set_of_lengths(inputs)
      set_y = set_of_lengths(targets)
      set_w = set_of_lengths(weights)
      if len(set_x) > 1:
          raise ValueError('All input arrays (x) should have '
                           'the same number of samples. Got array shapes: ' +
                           str([x.shape for x in inputs]))
      if len(set_y) > 1:
          raise ValueError('All target arrays (y) should have '
                           'the same number of samples. Got array shapes: ' +
                           str([y.shape for y in targets]))
      if set_x and set_y and list(set_x)[0] != list(set_y)[0]:
          raise ValueError('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 len(set_w) > 1:
          raise ValueError('All sample_weight arrays should have '
                           'the same number of samples. Got array shapes: ' +
                           str([w.shape for w in weights]))
      if set_y and set_w and list(set_y)[0] != list(set_w)[0]:
          raise ValueError('Sample_weight arrays should have '
                           'the same number of samples as target arrays. Got ' +
                           str(list(set_y)[0]) + ' input samples and ' +
                           str(list(set_w)[0]) + ' target samples.')
  
  
  def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
      """Does validation on the compatibility of targets and loss functions.
  
      This helps prevent users from using loss functions incorrectly.
  
      # Arguments
          targets: list of Numpy arrays of targets.
          loss_fns: list of loss functions.
          output_shapes: list of shapes of model outputs.
  
      # Raises
          ValueError: if a loss function or target array
              is incompatible with an output.
      """
      key_losses = {'mean_squared_error',
                    'binary_crossentropy',
                    'categorical_crossentropy'}
      for y, loss, shape in zip(targets, loss_fns, output_shapes):
          if loss is None:
              continue
          if loss.__name__ == 'categorical_crossentropy':
              if y.shape[-1] == 1:
                  raise ValueError(
                      '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:
              for target_dim, out_dim in zip(y.shape[1:], shape[1:]):
                  if out_dim is not None and target_dim != out_dim:
                      raise ValueError(
                          '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):
      """Maps metric functions to model outputs.
  
      # Arguments
          metrics: a list or dict of metric functions.
          output_names: a list of the names (strings) of model outputs.
  
      # Returns
          A list (one entry per model output) of lists of metric functions.
          For instance, if the model has 2 outputs, and for the first output
          we want to compute "binary_accuracy" and "binary_crossentropy",
          and just "binary_accuracy" for the second output,
          the list would look like:
              `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]`
  
      # Raises
          TypeError: if an incorrect type is passed for the `metrics` argument.
      """
      if not metrics:
          return [[] for _ in output_names]
      if isinstance(metrics, list):
          # we then apply all metrics to all outputs.
          return [copy.copy(metrics) for _ in output_names]
      elif isinstance(metrics, dict):
          nested_metrics = []
          for name in output_names:
              output_metrics = metrics.get(name, [])
              if not isinstance(output_metrics, list):
                  output_metrics = [output_metrics]
              nested_metrics.append(output_metrics)
          return nested_metrics
      else:
          raise TypeError('Type of `metrics` argument not understood. '
                          'Expected a list or dictionary, found: ' +
                          str(metrics))
  
  
  def _batch_shuffle(index_array, batch_size):
      """Shuffles an array in a batch-wise fashion.
  
      Useful for shuffling HDF5 arrays
      (where one cannot access arbitrary indices).
  
      # Arguments
          index_array: array of indices to be shuffled.
          batch_size: integer.
  
      # Returns
          The `index_array` array, shuffled in a batch-wise fashion.
      """
      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).
  
      # Arguments
          size: Integer, total size of the data to slice into batches.
          batch_size: Integer, batch size.
  
      # Returns
          A list of tuples of array indices.
      """
      num_batches = int(np.ceil(size / float(batch_size)))
      return [(i * batch_size, min(size, (i + 1) * batch_size))
              for i in range(0, num_batches)]
  
  
  def _slice_arrays(arrays, start=None, stop=None):
      """Slice an array or list of arrays.
  
      This takes an array-like, or a list of
      array-likes, and outputs:
          - arrays[start:stop] if `arrays` is an array-like
          - [x[start:stop] for x in arrays] if `arrays` is a list
  
      Can also work on list/array of indices: `_slice_arrays(x, indices)`
  
      # Arguments
          arrays: Single array or list of arrays.
          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.
  
      # Returns
          A slice of the array(s).
      """
      if arrays is None:
          return [None]
      elif isinstance(arrays, list):
          if hasattr(start, '__len__'):
              # hdf5 datasets only support list objects as indices
              if hasattr(start, 'shape'):
                  start = start.tolist()
              return [None if x is None else x[start] for x in arrays]
          else:
              return [None if x is None else x[start:stop] for x in arrays]
      else:
          if hasattr(start, '__len__'):
              if hasattr(start, 'shape'):
                  start = start.tolist()
              return arrays[start]
          elif hasattr(start, '__getitem__'):
              return arrays[start:stop]
          else:
              return [None]
  
  
  def _weighted_masked_objective(fn):
      """Adds support for masking and sample-weighting to an objective function.
  
      It transforms an objective function `fn(y_true, y_pred)`
      into a sample-weighted, cost-masked objective function
      `fn(y_true, y_pred, weights, mask)`.
  
      # Arguments
          fn: The objective function to wrap,
              with signature `fn(y_true, y_pred)`.
  
      # Returns
          A function with signature `fn(y_true, y_pred, weights, mask)`.
      """
      if fn is None:
          return None
  
      def weighted(y_true, y_pred, weights, mask=None):
          """Wrapper function.
  
          # Arguments
              y_true: `y_true` argument of `fn`.
              y_pred: `y_pred` argument of `fn`.
              weights: Weights tensor.
              mask: Mask tensor.
  
          # Returns
              Scalar tensor.
          """
          # 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)
  
          # apply sample weighting
          if weights is not None:
              # 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)))
              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 sample weight validation and standardization.
  
      Everything gets normalized to a single sample-wise (or timestep-wise)
      weight array.
  
      # Arguments
          y: Numpy array of model targets to be weighted.
          sample_weight: User-provided `sample_weight` argument.
          class_weight: User-provided `class_weight` argument.
          sample_weight_mode: One of `None` or `"temporal"`.
              `"temporal"` indicated that we expect 2D weight data
              that will be applied to the last 2 dimensions of
              the targets (i.e. we are weighting timesteps, not samples).
  
      # Returns
          A numpy array of target weights, one entry per sample to weight.
  
      # Raises
          ValueError: In case of invalid user-provided arguments.
      """
      if sample_weight_mode is not None:
          if sample_weight_mode != 'temporal':
              raise ValueError('"sample_weight_mode '
                               'should be None or "temporal". '
                               'Found: ' + str(sample_weight_mode))
          if len(y.shape) < 3:
              raise ValueError('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 ValueError('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 ValueError('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:
          if len(sample_weight.shape) > len(y.shape):
              raise ValueError('Found a sample_weight with shape' +
                               str(sample_weight.shape) + '.'
                               'Expected sample_weight with rank '
                               'less than or equal to ' + str(len(y.shape)))
  
          if y.shape[:sample_weight.ndim] != sample_weight.shape:
              raise ValueError('Found a sample_weight array with shape ' +
                               str(sample_weight.shape) + ' for an input with shape ' +
                               str(y.shape) + '. '
                               'sample_weight cannot be broadcast.')
          return sample_weight
      elif isinstance(class_weight, dict):
          if len(y.shape) > 2:
              raise ValueError('`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
                                if cls in class_weight])
  
          if len(weights) != len(y_classes):
              # subtract the sets to pick all missing classes
              existing_classes = set(y_classes)
              existing_class_weight = set(class_weight.keys())
              raise ValueError('`class_weight` must contain all classes in the data.'
                               ' The classes %s exist in the data but not in '
                               '`class_weight`.'
                               % (existing_classes - existing_class_weight))
          return weights
      else:
          if sample_weight_mode is None:
              return np.ones((y.shape[0],), dtype=K.floatx())
          else:
              return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
  
  
  class Model(Container):
      """The `Model` class adds training & evaluation routines to a `Container`.
      """
  
      def compile(self, optimizer, loss, metrics=None, loss_weights=None,
                  sample_weight_mode=None, weighted_metrics=None,
                  target_tensors=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 [losses](/losses).
                  If the model has multiple outputs, you can use a different loss
                  on each output by passing a dictionary or a list of losses.
                  The loss value that will be minimized by the model
                  will then be the sum of all individual losses.
              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'}`.
              loss_weights: Optional list or dictionary specifying scalar
                  coefficients (Python floats) to weight the loss contributions
                  of different model outputs.
                  The loss value that will be minimized by the model
                  will then be the *weighted sum* of all individual losses,
                  weighted by the `loss_weights` coefficients.
                  If a list, it is expected to have a 1:1 mapping
                  to the model's outputs. If a tensor, it is expected to map
                  output names (strings) to scalar coefficients.
              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.
              target_tensors: by default, Keras will create placeholders for the
                  model's target, which will be fed with the target data during
                  training. If instead you would like to use your own
                  target tensors (in turn, Keras will not expect external
                  Numpy data for these targets at training time), you
                  can specify them via the `target_tensors` argument. It can be
                  a single tensor (for a single-output model), a list of tensors,
                  or a dict mapping output names to target tensors.
              weighted_metrics: list of metrics to be evaluated and weighted
                  by sample_weight or class_weight during training and testing
              **kwargs: when using the Theano/CNTK backends, these arguments
                  are passed into K.function. When using the TensorFlow backend,
                  these arguments are passed into `tf.Session.run`.
  
          # Raises
              ValueError: In case of invalid arguments for
                  `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
          """
          loss = loss or {}
          self.optimizer = optimizers.get(optimizer)
          self.sample_weight_mode = sample_weight_mode
          self.loss = loss
          self.loss_weights = loss_weights
  
          # Prepare loss functions.
          if isinstance(loss, dict):
              for name in loss:
                  if name not in self.output_names:
                      raise ValueError('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:
                      warnings.warn('Output "' + name +
                                    '" missing from loss dictionary. '
                                    'We assume this was done on purpose, '
                                    'and we will not be expecting '
                                    'any data to be passed to "' + name +
                                    '" during training.', stacklevel=2)
                  loss_functions.append(losses.get(loss.get(name)))
          elif isinstance(loss, list):
              if len(loss) != len(self.outputs):
                  raise ValueError('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 = [losses.get(l) for l in loss]
          else:
              loss_function = losses.get(loss)
              loss_functions = [loss_function for _ in range(len(self.outputs))]
          self.loss_functions = loss_functions
          weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions]
          skip_target_indices = []
          skip_target_weighing_indices = []
          self._feed_outputs = []
          self._feed_output_names = []
          self._feed_output_shapes = []
          self._feed_loss_fns = []
          for i in range(len(weighted_losses)):
              if weighted_losses[i] is None:
                  skip_target_indices.append(i)
                  skip_target_weighing_indices.append(i)
  
          # Prepare output masks.
          masks = self.compute_mask(self.inputs, mask=None)
          if masks is None:
              masks = [None for _ in self.outputs]
          if not isinstance(masks, list):
              masks = [masks]
  
          # Prepare loss weights.
          if loss_weights is None:
              loss_weights_list = [1. for _ in range(len(self.outputs))]
          elif isinstance(loss_weights, dict):
              for name in loss_weights:
                  if name not in self.output_names:
                      raise ValueError('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 isinstance(loss_weights, list):
              if len(loss_weights) != len(self.outputs):
                  raise ValueError('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_weights))
              loss_weights_list = loss_weights
          else:
              raise TypeError('Could not interpret loss_weights argument: ' +
                              str(loss_weights) +
                              ' - expected a list of dicts.')
  
          # Prepare targets of model.
          self.targets = []
          self._feed_targets = []
          if target_tensors is not None:
              if isinstance(target_tensors, list):
                  if len(target_tensors) != len(self.outputs):
                      raise ValueError(
                          'When passing a list as `target_tensors`, '
                          'it should have one entry per model outputs. '
                          'The model has ' + str(len(self.outputs)) +
                          ' outputs, but you passed target_tensors=' +
                          str(target_tensors))
              elif isinstance(target_tensors, dict):
                  for name in target_tensors:
                      if name not in self.output_names:
                          raise ValueError('Unknown entry in `target_tensors` '
                                           'dictionary: "' + name + '". '
                                           'Only expected the following keys: ' +
                                           str(self.output_names))
                  _target_tensors = []
                  for name in self.output_names:
                      _target_tensors.append(target_tensors.get(name, None))
                  target_tensors = _target_tensors
              else:
                  raise TypeError('Expected `target_tensors` to be '
                                  'a list or dict, but got:', target_tensors)
          for i in range(len(self.outputs)):
              if i in skip_target_indices:
                  self.targets.append(None)
              else:
                  shape = self.internal_output_shapes[i]
                  name = self.output_names[i]
                  if target_tensors is not None:
                      target = target_tensors[i]
                  else:
                      target = None
                  if target is None or K.is_placeholder(target):
                      if target is None:
                          target = K.placeholder(ndim=len(shape),
                                                 name=name + '_target',
                                                 sparse=K.is_sparse(self.outputs[i]),
                                                 dtype=K.dtype(self.outputs[i]))
                      self._feed_targets.append(target)
                      self._feed_outputs.append(self.outputs[i])
                      self._feed_output_names.append(name)
                      self._feed_output_shapes.append(shape)
                      self._feed_loss_fns.append(self.loss_functions[i])
                  else:
                      skip_target_weighing_indices.append(i)
                  self.targets.append(target)
  
          # Prepare sample weights.
          sample_weights = []
          sample_weight_modes = []
          if isinstance(sample_weight_mode, dict):
              for name in sample_weight_mode:
                  if name not in self.output_names:
                      raise ValueError('Unknown entry in '
                                       'sample_weight_mode dictionary: "' +
                                       name + '". '
                                       'Only expected the following keys: ' +
                                       str(self.output_names))
              for i, name in enumerate(self.output_names):
                  if i in skip_target_weighing_indices:
                      weight = None
                      sample_weight_modes.append(None)
                  else:
                      if name not in sample_weight_mode:
                          raise ValueError('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 isinstance(sample_weight_mode, list):
              if len(sample_weight_mode) != len(self.outputs):
                  raise ValueError('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))
              for i in range(len(self.output_names)):
                  if i in skip_target_weighing_indices:
                      weight = None
                      sample_weight_modes.append(None)
                  else:
                      mode = sample_weight_mode[i]
                      name = self.output_names[i]
                      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:
              for i, name in enumerate(self.output_names):
                  if i in skip_target_weighing_indices:
                      sample_weight_modes.append(None)
                      sample_weights.append(None)
                  else:
                      if sample_weight_mode == 'temporal':
                          sample_weights.append(
                              K.placeholder(ndim=2,
                                            name=name + '_sample_weights'))
                          sample_weight_modes.append('temporal')
                      else:
                          sample_weights.append(
                              K.placeholder(ndim=1,
                                            name=name + '_sample_weights'))
                          sample_weight_modes.append(None)
          self.sample_weight_modes = sample_weight_modes
          self._feed_sample_weight_modes = []
          for i in range(len(self.outputs)):
              if i not in skip_target_weighing_indices:
                  self._feed_sample_weight_modes.append(self.sample_weight_modes[i])
  
          # Prepare metrics.
          self.metrics = metrics
          self.weighted_metrics = weighted_metrics
          self.metrics_names = ['loss']
          self.metrics_tensors = []
  
          # Compute total loss.
          total_loss = None
          with K.name_scope('loss'):
              for i in range(len(self.outputs)):
                  if i in skip_target_indices:
                      continue
                  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]
                  with K.name_scope(self.output_names[i] + '_loss'):
                      output_loss = weighted_loss(y_true, y_pred,
                                                  sample_weight, mask)
                  if len(self.outputs) > 1:
                      self.metrics_tensors.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
              if total_loss is None:
                  if not self.losses:
                      raise RuntimeError('The model cannot be compiled '
                                         'because it has no loss to optimize.')
                  else:
                      total_loss = 0.
  
              # Add regularization penalties
              # and other layer-specific losses.
              for loss_tensor in self.losses:
                  total_loss += loss_tensor
  
          # List of same size as output_names.
          # contains tuples (metrics for output, names of metrics).
          nested_metrics = _collect_metrics(metrics, self.output_names)
          nested_weighted_metrics = _collect_metrics(weighted_metrics, self.output_names)
  
          def append_metric(layer_index, metric_name, metric_tensor):
              """Helper function used in loop below."""
              if len(self.output_names) > 1:
                  metric_name = self.output_layers[layer_index].name + '_' + metric_name
              self.metrics_names.append(metric_name)
              self.metrics_tensors.append(metric_tensor)
  
          with K.name_scope('metrics'):
              for i in range(len(self.outputs)):
                  if i in skip_target_indices:
                      continue
  
                  y_true = self.targets[i]
                  y_pred = self.outputs[i]
                  weights = sample_weights[i]
                  output_metrics = nested_metrics[i]
                  output_weighted_metrics = nested_weighted_metrics[i]
  
                  def handle_metrics(metrics, weights=None):
                      metric_name_prefix = 'weighted_' if weights is not None else ''
  
                      for metric in 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 or
                                 self.loss_functions[i] == losses.binary_crossentropy):
                                  # case: binary accuracy
                                  acc_fn = metrics_module.binary_accuracy
                              elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
                                  # case: categorical accuracy with sparse targets
                                  acc_fn = metrics_module.sparse_categorical_accuracy
                              else:
                                  acc_fn = metrics_module.categorical_accuracy
  
                              weighted_metric_fn = _weighted_masked_objective(acc_fn)
                              metric_name = metric_name_prefix + 'acc'
                          else:
                              metric_fn = metrics_module.get(metric)
                              weighted_metric_fn = _weighted_masked_objective(metric_fn)
                              metric_name = metric_name_prefix + metric_fn.__name__
  
                          with K.name_scope(metric_name):
                              metric_result = weighted_metric_fn(y_true, y_pred,
                                                                 weights=weights,
                                                                 mask=masks[i])
                          append_metric(i, metric_name, metric_result)
  
                  handle_metrics(output_metrics)
                  handle_metrics(output_weighted_metrics, weights=weights)
  
          # Prepare gradient updates and state updates.
          self.total_loss = total_loss
          self.sample_weights = sample_weights
          self._feed_sample_weights = []
          for i in range(len(self.sample_weights)):
              if i not in skip_target_weighing_indices:
                  self._feed_sample_weights.append(sample_weights[i])
  
          # 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
  
          # Collected trainable weights, sorted in topological order.
          trainable_weights = self.trainable_weights
          self._collected_trainable_weights = trainable_weights
  
      def _make_train_function(self):
          if not hasattr(self, 'train_function'):
              raise RuntimeError('You must compile your model before using it.')
          if self.train_function is None:
              inputs = self._feed_inputs + self._feed_targets + self._feed_sample_weights
              if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                  inputs += [K.learning_phase()]
  
              with K.name_scope('training'):
                  with K.name_scope(self.optimizer.__class__.__name__):
                      training_updates = self.optimizer.get_updates(
                          params=self._collected_trainable_weights,
                          loss=self.total_loss)
                  updates = self.updates + training_updates
                  # Gets loss and metrics. Updates weights at each call.
                  self.train_function = K.function(inputs,
                                                   [self.total_loss] + self.metrics_tensors,
                                                   updates=updates,
                                                   name='train_function',
                                                   **self._function_kwargs)
  
      def _make_test_function(self):
          if not hasattr(self, 'test_function'):
              raise RuntimeError('You must compile your model before using it.')
          if self.test_function is None:
              inputs = self._feed_inputs + self._feed_targets + self._feed_sample_weights
              if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                  inputs += [K.learning_phase()]
              # Return loss and metrics, no gradient updates.
              # Does update the network states.
              self.test_function = K.function(inputs,
                                              [self.total_loss] + self.metrics_tensors,
                                              updates=self.state_updates,
                                              name='test_function',
                                              **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 and not isinstance(K.learning_phase(), int):
                  inputs = self._feed_inputs + [K.learning_phase()]
              else:
                  inputs = self._feed_inputs
              # Gets 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,
                                                 name='predict_function',
                                                 **kwargs)
  
      def _check_num_samples(self, ins, batch_size=None, steps=None, steps_name='steps'):
          """Determine the number of samples provided for training and evaluation.
  
          The number of samples is not defined when running with `steps`,
          in which case the number of samples is set to `None`.
  
          # Arguments
              ins: list of tensors to be fed to the Keras function.
              batch_size: integer batch size or `None` if not defined.
              steps: Total number of steps (batches of samples)
                  before declaring `_predict_loop` finished.
                  Ignored with the default value of `None`.
              steps_name: The public API's parameter name for `steps`.
  
          # Raises
              ValueError when `steps` is `None` and the attribute `ins.shape`
              does not exist. Also raises ValueError when `steps` is not `None`
              and `batch_size` is not `None` because they are mutually
              exclusive.
  
          # Returns
              When steps is `None`, returns the number of samples to be
              processed based on the size of the first dimension of the
              first input numpy array. When steps is not `None` and
              `batch_size` is `None`, returns `None`.
          """
          if steps is not None:
              num_samples = None
              if batch_size is not None:
                  raise ValueError('If ' + steps_name +
                                   ' is set the batch_size must be None.')
          elif ins and hasattr(ins[0], 'shape'):
              num_samples = ins[0].shape[0]
          else:
              raise ValueError('The input data should have shape, or '
                               'please specify ' + steps_name + '.')
          return num_samples
  
      def _fit_loop(self, f, ins, out_labels=None, batch_size=None,
                    epochs=100, verbose=1, callbacks=None,
                    val_f=None, val_ins=None, shuffle=True,
                    callback_metrics=None, initial_epoch=0,
                    steps_per_epoch=None, validation_steps=None):
          """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 or None if unknown.
              epochs: 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`.
              initial_epoch: epoch at which to start training
                  (useful for resuming a previous training run)
              steps_per_epoch: Total number of steps (batches of samples)
                  before declaring one epoch finished and starting the
                  next epoch. Ignored with the default value of `None`.
  
          # Returns
              `History` object.
          """
          do_validation = False
          if val_f and val_ins:
              do_validation = True
              if steps_per_epoch and not validation_steps:
                  if hasattr(ins[0], 'shape'):
                      validation_steps = steps_per_epoch
                  else:
                      raise ValueError('You must specify a value for '
                                       '`validation_steps` when using '
                                       '`steps_per_epoch` validation '
                                       'without a `shape` attribute.')
  
              if verbose and ins and hasattr(ins[0], 'shape'):
                  print('Train on %d samples, validate on %d samples' %
                        (ins[0].shape[0], val_ins[0].shape[0]))
  
          num_train_samples = self._check_num_samples(ins, batch_size,
                                                      steps_per_epoch, 'steps_per_epoch')
  
          if num_train_samples is not None:
              index_array = np.arange(num_train_samples)
  
          self.history = cbks.History()
          callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history]
          if verbose:
              if steps_per_epoch is not None:
                  count_mode = 'steps'
              else:
                  count_mode = 'samples'
              callbacks += [cbks.ProgbarLogger(count_mode)]
          callbacks = cbks.CallbackList(callbacks)
          out_labels = out_labels or []
  
          # 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,
              'epochs': epochs,
              'steps': steps_per_epoch,
              'samples': num_train_samples,
              'verbose': verbose,
              'do_validation': do_validation,
              'metrics': callback_metrics or [],
          })
          callbacks.on_train_begin()
          callback_model.stop_training = False
          for cbk in callbacks:
              cbk.validation_data = val_ins
  
          for epoch in range(initial_epoch, epochs):
              callbacks.on_epoch_begin(epoch)
              epoch_logs = {}
              if steps_per_epoch is not None:
                  for step_num in range(steps_per_epoch):
                      batch_logs = {}
                      batch_logs['batch'] = step_num
                      batch_logs['size'] = 1
                      callbacks.on_batch_begin(step_num, batch_logs)
                      outs = f(ins)
  
                      if not isinstance(outs, list):
                          outs = [outs]
                      for l, o in zip(out_labels, outs):
                          batch_logs[l] = o
  
                      callbacks.on_batch_end(step_num, batch_logs)
                      if callback_model.stop_training:
                          break
  
                  if do_validation:
                      val_outs = self._test_loop(val_f, val_ins,
                                                 batch_size=batch_size,
                                                 steps=validation_steps,
                                                 verbose=0)
                      if not isinstance(val_outs, list):
                          val_outs = [val_outs]
                      # Same labels assumed.
                      for l, o in zip(out_labels, val_outs):
                          epoch_logs['val_' + l] = o
              else:
                  if shuffle == 'batch':
                      index_array = _batch_shuffle(index_array, batch_size)
                  elif shuffle:
                      np.random.shuffle(index_array)
  
                  batches = _make_batches(num_train_samples, batch_size)
                  for batch_index, (batch_start, batch_end) in enumerate(batches):
                      batch_ids = index_array[batch_start:batch_end]
                      try:
                          if isinstance(ins[-1], float):
                              # Do not slice the training phase flag.
                              ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
                          else:
                              ins_batch = _slice_arrays(ins, batch_ids)
                      except TypeError:
                          raise TypeError('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 not isinstance(outs, list):
                          outs = [outs]
                      for l, o in zip(out_labels, outs):
                          batch_logs[l] = o
  
                      callbacks.on_batch_end(batch_index, batch_logs)
                      if callback_model.stop_training:
                          break
  
                      if batch_index == len(batches) - 1:  # Last batch.
                          if do_validation:
                              val_outs = self._test_loop(val_f, val_ins,
                                                         batch_size=batch_size,
                                                         verbose=0)
                              if not isinstance(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.
              steps: Total number of steps (batches of samples)
                  before declaring `_predict_loop` finished.
                  Ignored with the default value of `None`.
  
          # Returns
              Array of predictions (if the model has a single output)
              or list of arrays of predictions
              (if the model has multiple outputs).
          """
          if ins and hasattr(ins[0], 'shape'):
              samples = ins[0].shape[0]
          else:
              # May happen if we are running `predict` without Numpy input data,
              # i.e. if all inputs to the models are data tensors
              # instead of placeholders.
              # In that case we will run `predict` over a single batch.
              samples = batch_size
              verbose = 2
  
          outs = []
          if verbose == 1:
              progbar = Progbar(target=samples)
          batches = _make_batches(samples, batch_size)
          index_array = np.arange(samples)
          for batch_index, (batch_start, batch_end) in enumerate(batches):
              batch_ids = index_array[batch_start:batch_end]
              if ins and isinstance(ins[-1], float):
                  # Do not slice the training phase flag.
                  ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
              else:
                  ins_batch = _slice_arrays(ins, batch_ids)
  
              batch_outs = f(ins_batch)
              if not isinstance(batch_outs, list):
                  batch_outs = [batch_outs]
              if batch_index == 0:
                  for batch_out in batch_outs:
                      shape = (samples,) + batch_out.shape[1:]
                      outs.append(np.zeros(shape, dtype=batch_out.dtype))
  
              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=None, verbose=0, steps=None):
          """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 or `None`.
              verbose: verbosity mode.
              steps: Total number of steps (batches of samples)
                  before declaring predictions finished.
                  Ignored with the default value of `None`.
  
          # 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.
          """
          outs = []
          if batch_size is None and steps is not None:
              if verbose == 1:
                  progbar = Progbar(target=steps)
              for step_num in range(steps):
                  f(ins)
                  if verbose == 1:
                      progbar.update(step_num)
  
          else:
              if ins and hasattr(ins[0], 'shape'):
                  samples = ins[0].shape[0]
              else:
                  # May happen if we are running `predict` without Numpy input data,
                  # i.e. if all inputs to the models are data tensors
                  # instead of placeholders.
                  # In that case we will run `predict` over a single batch.
                  samples = batch_size
                  verbose = 2
  
              if verbose == 1:
                  progbar = Progbar(target=samples)
              batches = _make_batches(samples, batch_size)
              index_array = np.arange(samples)
              for batch_index, (batch_start, batch_end) in enumerate(batches):
                  batch_ids = index_array[batch_start:batch_end]
                  if isinstance(ins[-1], float):
                      # Do not slice the training phase flag.
                      ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
                  else:
                      ins_batch = _slice_arrays(ins, batch_ids)
  
                  batch_outs = f(ins_batch)
                  if isinstance(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 in range(len(outs)):
              outs[i] /= samples
          if len(outs) == 1:
              return outs[0]
          return outs
  
      def _standardize_user_data(self, x, y,
                                 sample_weight=None, class_weight=None,
                                 check_batch_axis=True, batch_size=None):
          if not hasattr(self, 'optimizer'):
              raise RuntimeError('You must compile a model before '
                                 'training/testing. '
                                 'Use `model.compile(optimizer, loss)`.')
  
          output_shapes = []
          for output_shape, loss_fn in zip(self._feed_output_shapes, self._feed_loss_fns):
              if loss_fn.__name__ == 'sparse_categorical_crossentropy':
                  output_shapes.append(output_shape[:-1] + (1,))
              elif getattr(losses, loss_fn.__name__, None) is None:
                  output_shapes.append(None)
              else:
                  output_shapes.append(output_shape)
          x = _standardize_input_data(x, self._feed_input_names,
                                      self._feed_input_shapes,
                                      check_batch_axis=False,
                                      exception_prefix='input')
          y = _standardize_input_data(y, self._feed_output_names,
                                      output_shapes,
                                      check_batch_axis=False,
                                      exception_prefix='target')
          sample_weights = _standardize_sample_weights(sample_weight,
                                                       self._feed_output_names)
          class_weights = _standardize_class_weights(class_weight,
                                                     self._feed_output_names)
          sample_weights = [_standardize_weights(ref, sw, cw, mode)
                            for (ref, sw, cw, mode)
                            in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]
          _check_array_lengths(x, y, sample_weights)
          _check_loss_and_target_compatibility(y,
                                               self._feed_loss_fns,
                                               self._feed_output_shapes)
          if self.stateful and batch_size:
              if x[0].shape[0] % batch_size != 0:
                  raise ValueError('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 _get_deduped_metrics_names(self):
          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)
          return deduped_out_labels
  
      def fit(self, x=None,
              y=None,
              batch_size=None,
              epochs=1,
              verbose=1,
              callbacks=None,
              validation_split=0.,
              validation_data=None,
              shuffle=True,
              class_weight=None,
              sample_weight=None,
              initial_epoch=0,
              steps_per_epoch=None,
              validation_steps=None,
              **kwargs):
          """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 or `None`. Number of samples per gradient update.
                  Defaults to 32 if training numpy arrays and no batch
                  size is specified. Defaults to `None` when `steps_per_epoch` is set.
              epochs: 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 (x_val, y_val, val_sample_weights).
              shuffle: boolean, whether to shuffle the training data
                  before each epoch. Has no effect when `steps_per_epoch`
                  is not `None`.
              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().
              initial_epoch: epoch at which to start training
                  (useful for resuming a previous training run)
              steps_per_epoch: Total number of steps (batches of samples)
                  before declaring one epoch finished and starting the
                  next epoch. When training with Input Tensors such as
                  TensorFlow data tensors, the default `None` is equal to
                  the number of unique samples in your dataset divided by
                  the batch size, or 1 if that cannot be determined.
              validation_steps: Only relevant if `steps_per_epoch`
                  is specified. Total number of steps (batches of samples)
                  to validate before stopping.
  
          # Returns
              A `History` instance. Its `history` attribute contains
              all information collected during training.
  
          # Raises
              ValueError: In case of mismatch between the provided input data
                  and what the model expects.
          """
          # backwards compatibility
          if batch_size is None and steps_per_epoch is None:
              batch_size = 32
          # Legacy support
          if 'nb_epoch' in kwargs:
              warnings.warn('The `nb_epoch` argument in `fit` '
                            'has been renamed `epochs`.', stacklevel=2)
              epochs = kwargs.pop('nb_epoch')
          if kwargs:
              raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
  
          # Validate user data.
          x, y, sample_weights = self._standardize_user_data(
              x, y,
              sample_weight=sample_weight,
              class_weight=class_weight,
              check_batch_axis=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 ValueError('When passing validation_data, '
                                   'it must contain 2 (x_val, y_val) '
                                   'or 3 (x_val, y_val, val_sample_weights) '
                                   'items, however it contains %d items' %
                                   len(validation_data))
  
              val_x, val_y, val_sample_weights = self._standardize_user_data(
                  val_x, val_y,
                  sample_weight=val_sample_weight,
                  check_batch_axis=False,
                  batch_size=batch_size)
              self._make_test_function()
              val_f = self.test_function
              if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                  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
              if hasattr(x[0], 'shape'):
                  split_at = int(x[0].shape[0] * (1. - validation_split))
              else:
                  split_at = int(len(x[0]) * (1. - validation_split))
              x, val_x = (_slice_arrays(x, 0, split_at), _slice_arrays(x, split_at))
              y, val_y = (_slice_arrays(y, 0, split_at), _slice_arrays(y, split_at))
              sample_weights, val_sample_weights = (
                  _slice_arrays(sample_weights, 0, split_at),
                  _slice_arrays(sample_weights, split_at))
              self._make_test_function()
              val_f = self.test_function
              if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                  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 and not isinstance(K.learning_phase(), int):
              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._get_deduped_metrics_names()
  
          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, epochs=epochs,
                                verbose=verbose, callbacks=callbacks,
                                val_f=val_f, val_ins=val_ins, shuffle=shuffle,
                                callback_metrics=callback_metrics,
                                initial_epoch=initial_epoch,
                                steps_per_epoch=steps_per_epoch,
                                validation_steps=validation_steps)
  
      def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
          """Returns the loss value & metrics values for the model in test mode.
  
          Computation is 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.
              verbose: verbosity mode, 0 or 1.
              sample_weight: Array of weights to weight the contribution
                  of different samples to the loss and metrics.
  
          # 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_axis=False,
              batch_size=batch_size)
          # Prepare inputs, delegate logic to `_test_loop`.
          if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
              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.
  
          Computation is done in batches.
  
          # 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
              Numpy array(s) of predictions.
  
          # Raises
              ValueError: In case of mismatch between the provided
                  input data and the model's expectations,
                  or in case a stateful model receives a number of samples
                  that is not a multiple of the batch size.
          """
          # Validate user data.
          x = _standardize_input_data(x, self._feed_input_names,
                                      self._feed_input_shapes,
                                      check_batch_axis=False)
          if self.stateful:
              if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
                  raise ValueError('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 and not isinstance(K.learning_phase(), int):
              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_axis=True)
          if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
              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_axis=True)
          if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
              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.
  
          # Arguments
              x: Input samples, as a Numpy array.
  
          # Returns
              Numpy array(s) of predictions.
          """
          x = _standardize_input_data(x, self._feed_input_names,
                                      self._feed_input_shapes)
          if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
              ins = x + [0.]
          else:
              ins = x
          self._make_predict_function()
          outputs = self.predict_function(ins)
          if len(outputs) == 1:
              return outputs[0]
          return outputs
  
      @interfaces.legacy_generator_methods_support
      def fit_generator(self, generator,
                        steps_per_epoch,
                        epochs=1,
                        verbose=1,
                        callbacks=None,
                        validation_data=None,
                        validation_steps=None,
                        class_weight=None,
                        max_queue_size=10,
                        workers=1,
                        use_multiprocessing=False,
                        shuffle=True,
                        initial_epoch=0):
          """Fits the model on data yielded 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.
  
          The use of `keras.utils.Sequence` guarantees the ordering
          and guarantees the single use of every input per epoch when
          using `use_multiprocessing=True`.
  
          # Arguments
              generator: a generator or an instance of Sequence (keras.utils.Sequence)
                      object in order to avoid duplicate data
                      when using multiprocessing.
                  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 `steps_per_epoch`
                  batches have been seen by the model.
              steps_per_epoch: Total number of steps (batches of samples)
                  to yield from `generator` before declaring one epoch
                  finished and starting the next epoch. It should typically
                  be equal to the number of unique samples if your dataset
                  divided by the batch size.
              epochs: 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).
              validation_steps: Only relevant if `validation_data`
                  is a generator. Total number of steps (batches of samples)
                  to yield from `generator` before stopping.
              class_weight: dictionary mapping class indices to a weight
                  for the class.
              max_queue_size: maximum size for the generator queue
              workers: maximum number of processes to spin up
                  when using process based threading
              use_multiprocessing: if True, use process based threading.
                  Note that because
                  this implementation relies on multiprocessing,
                  you should not pass
                  non picklable arguments to the generator
                  as they can't be passed
                  easily to children processes.
              shuffle: whether to shuffle the data at the beginning of each
                  epoch. Only used with instances of `Sequence` (
                  keras.utils.Sequence).
              initial_epoch: epoch at which to start training
                  (useful for resuming a previous training run)
  
          # 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'),
                                  steps_per_epoch=10000, epochs=10)
          ```
  
          # Raises
              ValueError: In case the generator yields
                  data in an invalid format.
          """
          wait_time = 0.01  # in seconds
          epoch = initial_epoch
  
          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__') or
                     isinstance(validation_data, Sequence))
          if val_gen and not validation_steps:
              raise ValueError('When using a generator for validation data, '
                               'you must specify a value for '
                               '`validation_steps`.')
  
          # Prepare display labels.
          out_labels = self._get_deduped_metrics_names()
          callback_metrics = out_labels + ['val_' + n for n in out_labels]
  
          # prepare callbacks
          self.history = cbks.History()
          callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history]
          if verbose:
              callbacks += [cbks.ProgbarLogger(count_mode='steps')]
          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({
              'epochs': epochs,
              'steps': steps_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 ValueError('`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)
              val_data = val_x + val_y + val_sample_weights
              if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
                  val_data += [0.]
              for cbk in callbacks:
                  cbk.validation_data = val_data
          is_sequence = isinstance(generator, Sequence)
          if not is_sequence and use_multiprocessing and workers > 1:
              warnings.warn(
                  UserWarning('Using a generator with `use_multiprocessing=True`'
                              ' and multiple workers may duplicate your data.'
                              ' Please consider using the`keras.utils.Sequence'
                              ' class.'))
          enqueuer = None
  
          try:
              if is_sequence:
                  enqueuer = OrderedEnqueuer(generator,
                                             use_multiprocessing=use_multiprocessing,
                                             shuffle=shuffle)
              else:
                  enqueuer = GeneratorEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing,
                                               wait_time=wait_time)
              enqueuer.start(workers=workers, max_queue_size=max_queue_size)
              output_generator = enqueuer.get()
  
              callback_model.stop_training = False
              while epoch < epochs:
                  callbacks.on_epoch_begin(epoch)
                  steps_done = 0
                  batch_index = 0
                  while steps_done < steps_per_epoch:
                      generator_output = next(output_generator)
  
                      if not hasattr(generator_output, '__len__'):
                          raise ValueError('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:
                          raise ValueError('Output of generator should be '
                                           'a tuple `(x, y, sample_weight)` '
                                           'or `(x, y)`. Found: ' +
                                           str(generator_output))
                      # build batch logs
                      batch_logs = {}
                      if isinstance(x, list):
                          batch_size = x[0].shape[0]
                      elif isinstance(x, dict):
                          batch_size = list(x.values())[0].shape[0]
                      else:
                          batch_size = x.shape[0]
                      batch_logs['batch'] = batch_index
                      batch_logs['size'] = batch_size
                      callbacks.on_batch_begin(batch_index, batch_logs)
  
                      outs = self.train_on_batch(x, y,
                                                 sample_weight=sample_weight,
                                                 class_weight=class_weight)
  
                      if not isinstance(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
                      steps_done += 1
  
                      # Epoch finished.
                      if steps_done >= steps_per_epoch and do_validation:
                          if val_gen:
                              val_outs = self.evaluate_generator(
                                  validation_data,
                                  validation_steps,
                                  max_queue_size=max_queue_size,
                                  workers=workers,
                                  use_multiprocessing=use_multiprocessing)
                          else:
                              # No need for try/except because
                              # data has already been validated.
                              val_outs = self.evaluate(
                                  val_x, val_y,
                                  batch_size=batch_size,
                                  sample_weight=val_sample_weights,
                                  verbose=0)
                          if not isinstance(val_outs, list):
                              val_outs = [val_outs]
                          # Same labels assumed.
                          for l, o in zip(out_labels, val_outs):
                              epoch_logs['val_' + l] = o
  
                      if callback_model.stop_training:
                          break
  
                  callbacks.on_epoch_end(epoch, epoch_logs)
                  epoch += 1
                  if callback_model.stop_training:
                      break
  
          finally:
              if enqueuer is not None:
                  enqueuer.stop()
  
          callbacks.on_train_end()
          return self.history
  
      @interfaces.legacy_generator_methods_support
      def evaluate_generator(self, generator, steps,
                             max_queue_size=10,
                             workers=1,
                             use_multiprocessing=False):
          """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)
                  or an instance of Sequence (keras.utils.Sequence)
                      object in order to avoid duplicate data
                      when using multiprocessing.
              steps: Total number of steps (batches of samples)
                  to yield from `generator` before stopping.
              max_queue_size: maximum size for the generator queue
              workers: maximum number of processes to spin up
                  when using process based threading
              use_multiprocessing: if True, use process based threading.
                  Note that because
                  this implementation relies on multiprocessing,
                  you should not pass
                  non picklable arguments to the generator
                  as they can't be passed
                  easily to children processes.
  
          # 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.
  
          # Raises
              ValueError: In case the generator yields
                  data in an invalid format.
          """
          self._make_test_function()
  
          steps_done = 0
          wait_time = 0.01
          all_outs = []
          batch_sizes = []
          is_sequence = isinstance(generator, Sequence)
          if not is_sequence and use_multiprocessing and workers > 1:
              warnings.warn(
                  UserWarning('Using a generator with `use_multiprocessing=True`'
                              ' and multiple workers may duplicate your data.'
                              ' Please consider using the`keras.utils.Sequence'
                              ' class.'))
          enqueuer = None
  
          try:
              if is_sequence:
                  enqueuer = OrderedEnqueuer(generator,
                                             use_multiprocessing=use_multiprocessing)
              else:
                  enqueuer = GeneratorEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing,
                                               wait_time=wait_time)
              enqueuer.start(workers=workers, max_queue_size=max_queue_size)
              output_generator = enqueuer.get()
  
              while steps_done < steps:
                  generator_output = next(output_generator)
                  if not hasattr(generator_output, '__len__'):
                      raise ValueError('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:
                      raise ValueError('Output of generator should be a tuple '
                                       '(x, y, sample_weight) '
                                       'or (x, y). Found: ' +
                                       str(generator_output))
                  outs = self.test_on_batch(x, y, sample_weight=sample_weight)
  
                  if isinstance(x, list):
                      batch_size = len(x[0])
                  elif isinstance(x, dict):
                      batch_size = len(list(x.values())[0])
                  else:
                      batch_size = len(x)
                  if batch_size == 0:
                      raise ValueError('Received an empty batch. '
                                       'Batches should at least contain one item.')
                  all_outs.append(outs)
  
                  steps_done += 1
                  batch_sizes.append(batch_size)
  
          finally:
              if enqueuer is not None:
                  enqueuer.stop()
  
          if not isinstance(outs, list):
              return np.average(np.asarray(all_outs),
                                weights=batch_sizes)
          else:
              averages = []
              for i in range(len(outs)):
                  averages.append(np.average([out[i] for out in all_outs],
                                             weights=batch_sizes))
              return averages
  
      @interfaces.legacy_generator_methods_support
      def predict_generator(self, generator, steps,
                            max_queue_size=10,
                            workers=1,
                            use_multiprocessing=False,
                            verbose=0):
          """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
                      or an instance of Sequence (keras.utils.Sequence)
                      object in order to avoid duplicate data
                      when using multiprocessing.
              steps: Total number of steps (batches of samples)
                  to yield from `generator` before stopping.
              max_queue_size: Maximum size for the generator queue.
              workers: Maximum number of processes to spin up
                  when using process based threading
              use_multiprocessing: If `True`, use process based threading.
                  Note that because
                  this implementation relies on multiprocessing,
                  you should not pass
                  non picklable arguments to the generator
                  as they can't be passed
                  easily to children processes.
              verbose: verbosity mode, 0 or 1.
  
          # Returns
              Numpy array(s) of predictions.
  
          # Raises
              ValueError: In case the generator yields
                  data in an invalid format.
          """
          self._make_predict_function()
  
          steps_done = 0
          wait_time = 0.01
          all_outs = []
          is_sequence = isinstance(generator, Sequence)
          if not is_sequence and use_multiprocessing and workers > 1:
              warnings.warn(
                  UserWarning('Using a generator with `use_multiprocessing=True`'
                              ' and multiple workers may duplicate your data.'
                              ' Please consider using the`keras.utils.Sequence'
                              ' class.'))
          enqueuer = None
  
          try:
              if is_sequence:
                  enqueuer = OrderedEnqueuer(generator,
                                             use_multiprocessing=use_multiprocessing)
              else:
                  enqueuer = GeneratorEnqueuer(generator,
                                               use_multiprocessing=use_multiprocessing,
                                               wait_time=wait_time)
              enqueuer.start(workers=workers, max_queue_size=max_queue_size)
              output_generator = enqueuer.get()
  
              if verbose == 1:
                  progbar = Progbar(target=steps)
  
              while steps_done < steps:
                  generator_output = next(output_generator)
                  if isinstance(generator_output, tuple):
                      # Compatibility with the generators
                      # used for training.
                      if len(generator_output) == 2:
                          x, _ = generator_output
                      elif len(generator_output) == 3:
                          x, _, _ = generator_output
                      else:
                          raise ValueError('Output of generator should be '
                                           'a tuple `(x, y, sample_weight)` '
                                           'or `(x, y)`. Found: ' +
                                           str(generator_output))
                  else:
                      # Assumes a generator that only
                      # yields inputs (not targets and sample weights).
                      x = generator_output
  
                  outs = self.predict_on_batch(x)
                  if not isinstance(outs, list):
                      outs = [outs]
  
                  if not all_outs:
                      for out in outs:
                          all_outs.append([])
  
                  for i, out in enumerate(outs):
                      all_outs[i].append(out)
                  steps_done += 1
                  if verbose == 1:
                      progbar.update(steps_done)
  
          finally:
              if enqueuer is not None:
                  enqueuer.stop()
  
          if len(all_outs) == 1:
              if steps_done == 1:
                  return all_outs[0][0]
              else:
                  return np.concatenate(all_outs[0])
          if steps_done == 1:
              return [out for out in all_outs]
          else:
              return [np.concatenate(out) for out in all_outs]
  