text: """Model saving utilities.
  """
  from __future__ import print_function
  from __future__ import absolute_import
  from __future__ import division
  
  import numpy as np
  import os
  import json
  import yaml
  import warnings
  from six.moves import zip
  
  from .. import backend as K
  from .. import optimizers
  from ..utils.io_utils import ask_to_proceed_with_overwrite
  from ..utils import conv_utils
  
  try:
      import h5py
      HDF5_OBJECT_HEADER_LIMIT = 64512
  except ImportError:
      h5py = None
  
  
  def save_model(model, filepath, overwrite=True, include_optimizer=True):
      """Save a model to a HDF5 file.
  
      Note: Please also see
      [How can I install HDF5 or h5py to save my models in Keras?](
          /getting-started/faq/
          #how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras)
      in the FAQ for instructions on how to install `h5py`.
  
      The saved model contains:
          - the model's configuration (topology)
          - the model's weights
          - the model's optimizer's state (if any)
  
      Thus the saved model can be reinstantiated in
      the exact same state, without any of the code
      used for model definition or training.
  
      # Arguments
          model: Keras model instance to be saved.
          filepath: one of the following:
              - string, path where to save the model, or
              - h5py.File object where to save the model
          overwrite: Whether we should overwrite any existing
              model at the target location, or instead
              ask the user with a manual prompt.
          include_optimizer: If True, save optimizer's state together.
  
      # Raises
          ImportError: if h5py is not available.
      """
  
      if h5py is None:
          raise ImportError('`save_model` requires h5py.')
  
      def get_json_type(obj):
          """Serialize any object to a JSON-serializable structure.
  
          # Arguments
              obj: the object to serialize
  
          # Returns
              JSON-serializable structure representing `obj`.
  
          # Raises
              TypeError: if `obj` cannot be serialized.
          """
          # if obj is a serializable Keras class instance
          # e.g. optimizer, layer
          if hasattr(obj, 'get_config'):
              return {'class_name': obj.__class__.__name__,
                      'config': obj.get_config()}
  
          # if obj is any numpy type
          if type(obj).__module__ == np.__name__:
              if isinstance(obj, np.ndarray):
                  return {'type': type(obj),
                          'value': obj.tolist()}
              else:
                  return obj.item()
  
          # misc functions (e.g. loss function)
          if callable(obj):
              return obj.__name__
  
          # if obj is a python 'type'
          if type(obj).__name__ == type.__name__:
              return obj.__name__
  
          raise TypeError('Not JSON Serializable:', obj)
  
      from .. import __version__ as keras_version
  
      if not isinstance(filepath, h5py.File):
          # If file exists and should not be overwritten.
          if not overwrite and os.path.isfile(filepath):
              proceed = ask_to_proceed_with_overwrite(filepath)
              if not proceed:
                  return
  
          f = h5py.File(filepath, mode='w')
          opened_new_file = True
      else:
          f = filepath
          opened_new_file = False
  
      try:
          f.attrs['keras_version'] = str(keras_version).encode('utf8')
          f.attrs['backend'] = K.backend().encode('utf8')
          f.attrs['model_config'] = json.dumps({
              'class_name': model.__class__.__name__,
              'config': model.get_config()
          }, default=get_json_type).encode('utf8')
  
          model_weights_group = f.create_group('model_weights')
          model_layers = model.layers
          save_weights_to_hdf5_group(model_weights_group, model_layers)
  
          if include_optimizer and model.optimizer:
              if isinstance(model.optimizer, optimizers.TFOptimizer):
                  warnings.warn(
                      'TensorFlow optimizers do not '
                      'make it possible to access '
                      'optimizer attributes or optimizer state '
                      'after instantiation. '
                      'As a result, we cannot save the optimizer '
                      'as part of the model save file.'
                      'You will have to compile your model again '
                      'after loading it. '
                      'Prefer using a Keras optimizer instead '
                      '(see keras.io/optimizers).')
              else:
                  f.attrs['training_config'] = json.dumps({
                      'optimizer_config': {
                          'class_name': model.optimizer.__class__.__name__,
                          'config': model.optimizer.get_config()
                      },
                      'loss': model.loss,
                      'metrics': model.metrics,
                      'sample_weight_mode': model.sample_weight_mode,
                      'loss_weights': model.loss_weights,
                  }, default=get_json_type).encode('utf8')
  
                  # Save optimizer weights.
                  symbolic_weights = getattr(model.optimizer, 'weights')
                  if symbolic_weights:
                      optimizer_weights_group = f.create_group(
                          'optimizer_weights')
                      weight_values = K.batch_get_value(symbolic_weights)
                      weight_names = []
                      for i, (w, val) in enumerate(zip(symbolic_weights,
                                                       weight_values)):
                          # Default values of symbolic_weights is /variable
                          # for Theano and CNTK
                          if K.backend() == 'theano' or K.backend() == 'cntk':
                              if hasattr(w, 'name'):
                                  if w.name.split('/')[-1] == 'variable':
                                      name = str(w.name) + '_' + str(i)
                                  else:
                                      name = str(w.name)
                              else:
                                  name = 'param_' + str(i)
                          else:
                              if hasattr(w, 'name') and w.name:
                                  name = str(w.name)
                              else:
                                  name = 'param_' + str(i)
                          weight_names.append(name.encode('utf8'))
                      optimizer_weights_group.attrs[
                          'weight_names'] = weight_names
                      for name, val in zip(weight_names, weight_values):
                          param_dset = optimizer_weights_group.create_dataset(
                              name,
                              val.shape,
                              dtype=val.dtype)
                          if not val.shape:
                              # scalar
                              param_dset[()] = val
                          else:
                              param_dset[:] = val
          f.flush()
      finally:
          if opened_new_file:
              f.close()
  
  
  def load_model(filepath, custom_objects=None, compile=True):
      """Loads a model saved via `save_model`.
  
      # Arguments
          filepath: one of the following:
              - string, path to the saved model, or
              - h5py.File object from which to load the model
          custom_objects: Optional dictionary mapping names
              (strings) to custom classes or functions to be
              considered during deserialization.
          compile: Boolean, whether to compile the model
              after loading.
  
      # Returns
          A Keras model instance. If an optimizer was found
          as part of the saved model, the model is already
          compiled. Otherwise, the model is uncompiled and
          a warning will be displayed. When `compile` is set
          to False, the compilation is omitted without any
          warning.
  
      # Raises
          ImportError: if h5py is not available.
          ValueError: In case of an invalid savefile.
      """
      if h5py is None:
          raise ImportError('`load_model` requires h5py.')
  
      if not custom_objects:
          custom_objects = {}
  
      def convert_custom_objects(obj):
          """Handles custom object lookup.
  
          # Arguments
              obj: object, dict, or list.
  
          # Returns
              The same structure, where occurrences
                  of a custom object name have been replaced
                  with the custom object.
          """
          if isinstance(obj, list):
              deserialized = []
              for value in obj:
                  deserialized.append(convert_custom_objects(value))
              return deserialized
          if isinstance(obj, dict):
              deserialized = {}
              for key, value in obj.items():
                  deserialized[key] = convert_custom_objects(value)
              return deserialized
          if obj in custom_objects:
              return custom_objects[obj]
          return obj
  
      opened_new_file = not isinstance(filepath, h5py.File)
      if opened_new_file:
          f = h5py.File(filepath, mode='r')
      else:
          f = filepath
  
      model = None
      try:
          # instantiate model
          model_config = f.attrs.get('model_config')
          if model_config is None:
              raise ValueError('No model found in config file.')
          model_config = json.loads(model_config.decode('utf-8'))
          model = model_from_config(model_config, custom_objects=custom_objects)
  
          # set weights
          load_weights_from_hdf5_group(f['model_weights'], model.layers)
  
          if compile:
              # instantiate optimizer
              training_config = f.attrs.get('training_config')
              if training_config is None:
                  warnings.warn('No training configuration found in save file: '
                                'the model was *not* compiled. '
                                'Compile it manually.')
                  return model
              training_config = json.loads(training_config.decode('utf-8'))
              optimizer_config = training_config['optimizer_config']
              optimizer = optimizers.deserialize(optimizer_config,
                                                 custom_objects=custom_objects)
  
              # Recover loss functions and metrics.
              loss = convert_custom_objects(training_config['loss'])
              metrics = convert_custom_objects(training_config['metrics'])
              sample_weight_mode = training_config['sample_weight_mode']
              loss_weights = training_config['loss_weights']
  
              # Compile model.
              model.compile(optimizer=optimizer,
                            loss=loss,
                            metrics=metrics,
                            loss_weights=loss_weights,
                            sample_weight_mode=sample_weight_mode)
  
              # Set optimizer weights.
              if 'optimizer_weights' in f:
                  # Build train function (to get weight updates).
                  if model.__class__.__name__ == 'Sequential':
                      model.model._make_train_function()
                  else:
                      model._make_train_function()
                  optimizer_weights_group = f['optimizer_weights']
                  optimizer_weight_names = [
                      n.decode('utf8') for n in
                      optimizer_weights_group.attrs['weight_names']]
                  optimizer_weight_values = [optimizer_weights_group[n] for n in
                                             optimizer_weight_names]
                  try:
                      model.optimizer.set_weights(optimizer_weight_values)
                  except ValueError:
                      warnings.warn('Error in loading the saved optimizer '
                                    'state. As a result, your model is '
                                    'starting with a freshly initialized '
                                    'optimizer.')
      finally:
          if opened_new_file:
              f.close()
      return model
  
  
  def model_from_config(config, custom_objects=None):
      """Instantiates a Keras model from its config.
  
      # Arguments
          config: Configuration dictionary.
          custom_objects: Optional dictionary mapping names
              (strings) to custom classes or functions to be
              considered during deserialization.
  
      # Returns
          A Keras model instance (uncompiled).
  
      # Raises
          TypeError: if `config` is not a dictionary.
      """
      if isinstance(config, list):
          raise TypeError('`model_from_config` expects a dictionary, '
                          'not a list. Maybe you meant to use '
                          '`Sequential.from_config(config)`?')
      from ..layers import deserialize
      return deserialize(config, custom_objects=custom_objects)
  
  
  def model_from_yaml(yaml_string, custom_objects=None):
      """Parses a yaml model configuration file and returns a model instance.
  
      # Arguments
          yaml_string: YAML string encoding a model configuration.
          custom_objects: Optional dictionary mapping names
              (strings) to custom classes or functions to be
              considered during deserialization.
  
      # Returns
          A Keras model instance (uncompiled).
      """
      config = yaml.load(yaml_string)
      from ..layers import deserialize
      return deserialize(config, custom_objects=custom_objects)
  
  
  def model_from_json(json_string, custom_objects=None):
      """Parses a JSON model configuration file and returns a model instance.
  
      # Arguments
          json_string: JSON string encoding a model configuration.
          custom_objects: Optional dictionary mapping names
              (strings) to custom classes or functions to be
              considered during deserialization.
  
      # Returns
          A Keras model instance (uncompiled).
      """
      config = json.loads(json_string)
      from ..layers import deserialize
      return deserialize(config, custom_objects=custom_objects)
  
  
  def save_attributes_to_hdf5_group(group, name, data):
      """Saves attributes (data) of the specified name into the HDF5 group.
  
      This method deals with an inherent problem of HDF5 file which is not
      able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
  
      # Arguments
          group: A pointer to a HDF5 group.
          name: A name of the attributes to save.
          data: Attributes data to store.
      """
      # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
      # because in that case even chunking the array would not make the saving
      # possible.
      bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]
  
      # Expecting this to never be true.
      if len(bad_attributes) > 0:
          raise RuntimeError('The following attributes cannot be saved to HDF5 '
                             'file because they are larger than %d bytes: %s'
                             % (HDF5_OBJECT_HEADER_LIMIT,
                                ', '.join([x for x in bad_attributes])))
  
      data_npy = np.asarray(data)
  
      num_chunks = 1
      chunked_data = np.array_split(data_npy, num_chunks)
  
      # This will never loop forever thanks to the test above.
      while any(map(lambda x: x.nbytes > HDF5_OBJECT_HEADER_LIMIT, chunked_data)):
          num_chunks += 1
          chunked_data = np.array_split(data_npy, num_chunks)
  
      if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(chunked_data):
              group.attrs['%s%d' % (name, chunk_id)] = chunk_data
      else:
          group.attrs[name] = data
  
  
  def load_attributes_from_hdf5_group(group, name):
      """Loads attributes of the specified name from the HDF5 group.
  
      This method deals with an inherent problem
      of HDF5 file which is not able to store
      data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
  
      # Arguments
          group: A pointer to a HDF5 group.
          name: A name of the attributes to load.
  
      # Returns
          data: Attributes data.
      """
      if name in group.attrs:
          data = [n.decode('utf8') for n in group.attrs[name]]
      else:
          data = []
          chunk_id = 0
          while ('%s%d' % (name, chunk_id)) in group.attrs:
              data.extend([n.decode('utf8')
                           for n in group.attrs['%s%d' % (name, chunk_id)]])
              chunk_id += 1
      return data
  
  
  def save_weights_to_hdf5_group(f, layers):
      from .. import __version__ as keras_version
  
      save_attributes_to_hdf5_group(
          f, 'layer_names', [layer.name.encode('utf8') for layer in layers])
      f.attrs['backend'] = K.backend().encode('utf8')
      f.attrs['keras_version'] = str(keras_version).encode('utf8')
  
      for layer in layers:
          g = f.create_group(layer.name)
          symbolic_weights = layer.weights
          weight_values = K.batch_get_value(symbolic_weights)
          weight_names = []
          for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
              if hasattr(w, 'name') and w.name:
                  name = str(w.name)
              else:
                  name = 'param_' + str(i)
              weight_names.append(name.encode('utf8'))
          save_attributes_to_hdf5_group(g, 'weight_names', weight_names)
          for name, val in zip(weight_names, weight_values):
              param_dset = g.create_dataset(name, val.shape,
                                            dtype=val.dtype)
              if not val.shape:
                  # scalar
                  param_dset[()] = val
              else:
                  param_dset[:] = val
  
  
  def preprocess_weights_for_loading(layer, weights,
                                     original_keras_version=None,
                                     original_backend=None,
                                     reshape=False):
      """Converts layers weights from Keras 1 format to Keras 2 and also weights of CuDNN layers in Keras 2.
  
      # Arguments
          layer: Layer instance.
          weights: List of weights values (Numpy arrays).
          original_keras_version: Keras version for the weights, as a string.
          original_backend: Keras backend the weights were trained with,
              as a string.
          reshape: Reshape weights to fit the layer when the correct number
              of values are present but the shape does not match.
  
      # Returns
          A list of weights values (Numpy arrays).
      """
      def convert_nested_bidirectional(weights):
          """Converts layers nested in `Bidirectional` wrapper by `preprocess_weights_for_loading()`.
  
          # Arguments
              weights: List of weights values (Numpy arrays).
          # Returns
              A list of weights values (Numpy arrays).
          """
          num_weights_per_layer = len(weights) // 2
          forward_weights = preprocess_weights_for_loading(layer.forward_layer,
                                                           weights[:num_weights_per_layer],
                                                           original_keras_version,
                                                           original_backend)
          backward_weights = preprocess_weights_for_loading(layer.backward_layer,
                                                            weights[num_weights_per_layer:],
                                                            original_keras_version,
                                                            original_backend)
          return forward_weights + backward_weights
  
      def convert_nested_model(weights):
          """Converts layers nested in `Model` or `Sequential` by `preprocess_weights_for_loading()`.
  
          # Arguments
              weights: List of weights values (Numpy arrays).
          # Returns
              A list of weights values (Numpy arrays).
          """
          new_weights = []
          # trainable weights
          for sublayer in layer.layers:
              num_weights = len(sublayer.trainable_weights)
              if num_weights > 0:
                  new_weights.extend(preprocess_weights_for_loading(
                      layer=sublayer,
                      weights=weights[:num_weights],
                      original_keras_version=original_keras_version,
                      original_backend=original_backend))
                  weights = weights[num_weights:]
  
          # non-trainable weights
          for sublayer in layer.layers:
              num_weights = len([l for l in sublayer.weights
                                 if l not in sublayer.trainable_weights])
              if num_weights > 0:
                  new_weights.extend(preprocess_weights_for_loading(
                      layer=sublayer,
                      weights=weights[:num_weights],
                      original_keras_version=original_keras_version,
                      original_backend=original_backend))
                  weights = weights[num_weights:]
          return new_weights
  
      # Convert layers nested in Bidirectional/Model/Sequential.
      # Both transformation should be ran for both Keras 1->2 conversion
      # and for conversion of CuDNN layers.
      if layer.__class__.__name__ == 'Bidirectional':
          weights = convert_nested_bidirectional(weights)
      elif layer.__class__.__name__ in ['Model', 'Sequential']:
          weights = convert_nested_model(weights)
  
      if original_keras_version == '1':
          if layer.__class__.__name__ == 'TimeDistributed':
              weights = preprocess_weights_for_loading(layer.layer,
                                                       weights,
                                                       original_keras_version,
                                                       original_backend)
  
          if layer.__class__.__name__ == 'Conv1D':
              shape = weights[0].shape
              # Handle Keras 1.1 format
              if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters:
                  # Legacy shape:
                  # (filters, input_dim, filter_length, 1)
                  assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0], 1)
                  weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
              weights[0] = weights[0][:, 0, :, :]
  
          if layer.__class__.__name__ == 'Conv2D':
              if layer.data_format == 'channels_first':
                  # old: (filters, stack_size, kernel_rows, kernel_cols)
                  # new: (kernel_rows, kernel_cols, stack_size, filters)
                  weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
  
          if layer.__class__.__name__ == 'Conv2DTranspose':
              if layer.data_format == 'channels_last':
                  # old: (kernel_rows, kernel_cols, stack_size, filters)
                  # new: (kernel_rows, kernel_cols, filters, stack_size)
                  weights[0] = np.transpose(weights[0], (0, 1, 3, 2))
              if layer.data_format == 'channels_first':
                  # old: (filters, stack_size, kernel_rows, kernel_cols)
                  # new: (kernel_rows, kernel_cols, filters, stack_size)
                  weights[0] = np.transpose(weights[0], (2, 3, 0, 1))
  
          if layer.__class__.__name__ == 'Conv3D':
              if layer.data_format == 'channels_first':
                  # old: (filters, stack_size, ...)
                  # new: (..., stack_size, filters)
                  weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0))
  
          if layer.__class__.__name__ == 'GRU':
              if len(weights) == 9:
                  kernel = np.concatenate([weights[0],
                                           weights[3],
                                           weights[6]], axis=-1)
                  recurrent_kernel = np.concatenate([weights[1],
                                                     weights[4],
                                                     weights[7]], axis=-1)
                  bias = np.concatenate([weights[2],
                                         weights[5],
                                         weights[8]], axis=-1)
                  weights = [kernel, recurrent_kernel, bias]
  
          if layer.__class__.__name__ == 'LSTM':
              if len(weights) == 12:
                  # old: i, c, f, o
                  # new: i, f, c, o
                  kernel = np.concatenate([weights[0],
                                           weights[6],
                                           weights[3],
                                           weights[9]], axis=-1)
                  recurrent_kernel = np.concatenate([weights[1],
                                                     weights[7],
                                                     weights[4],
                                                     weights[10]], axis=-1)
                  bias = np.concatenate([weights[2],
                                         weights[8],
                                         weights[5],
                                         weights[11]], axis=-1)
                  weights = [kernel, recurrent_kernel, bias]
  
          if layer.__class__.__name__ == 'ConvLSTM2D':
              if len(weights) == 12:
                  kernel = np.concatenate([weights[0],
                                           weights[6],
                                           weights[3],
                                           weights[9]], axis=-1)
                  recurrent_kernel = np.concatenate([weights[1],
                                                     weights[7],
                                                     weights[4],
                                                     weights[10]], axis=-1)
                  bias = np.concatenate([weights[2],
                                         weights[8],
                                         weights[5],
                                         weights[11]], axis=-1)
                  if layer.data_format == 'channels_first':
                      # old: (filters, stack_size, kernel_rows, kernel_cols)
                      # new: (kernel_rows, kernel_cols, stack_size, filters)
                      kernel = np.transpose(kernel, (2, 3, 1, 0))
                      recurrent_kernel = np.transpose(recurrent_kernel,
                                                      (2, 3, 1, 0))
                  weights = [kernel, recurrent_kernel, bias]
  
      conv_layers = ['Conv1D',
                     'Conv2D',
                     'Conv3D',
                     'Conv2DTranspose',
                     'ConvLSTM2D']
      if layer.__class__.__name__ in conv_layers:
          layer_weights_shape = K.int_shape(layer.weights[0])
          if _need_convert_kernel(original_backend):
              weights[0] = conv_utils.convert_kernel(weights[0])
              if layer.__class__.__name__ == 'ConvLSTM2D':
                  weights[1] = conv_utils.convert_kernel(weights[1])
          if reshape and layer_weights_shape != weights[0].shape:
              if weights[0].size != np.prod(layer_weights_shape):
                  raise ValueError('Weights must be of equal size to ' +
                                   'apply a reshape operation. ' +
                                   'Layer ' + layer.name +
                                   '\'s weights have shape ' +
                                   str(layer_weights_shape) + ' and size ' +
                                   str(np.prod(layer_weights_shape)) + '. ' +
                                   'The weights for loading have shape ' +
                                   str(weights[0].shape) + ' and size ' +
                                   str(weights[0].size) + '. ')
              weights[0] = np.reshape(weights[0], layer_weights_shape)
          elif layer_weights_shape != weights[0].shape:
              weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
              if layer.__class__.__name__ == 'ConvLSTM2D':
                  weights[1] = np.transpose(weights[1], (3, 2, 0, 1))
  
      # convert CuDNN layers
      weights = _convert_rnn_weights(layer, weights)
  
      return weights
  
  
  def _convert_rnn_weights(layer, weights):
      """Converts weights for RNN layers between native and CuDNN format.
  
      Input kernels for each gate are transposed and converted between Fortran
      and C layout, recurrent kernels are transposed. For LSTM biases are summed/
      split in half, for GRU biases are reshaped.
  
      Weights can be converted in both directions between `LSTM` and`CuDNNSLTM`
      and between `CuDNNGRU` and `GRU(reset_after=True)`. Default `GRU` is not
      compatible with `CuDNNGRU`.
  
      For missing biases in `LSTM`/`GRU` (`use_bias=False`),
      no conversion is made.
  
      # Arguments
          layer: Target layer instance.
          weights: List of source weights values (input kernels, recurrent
              kernels, [biases]) (Numpy arrays).
  
      # Returns
          A list of converted weights values (Numpy arrays).
  
      # Raises
          ValueError: for incompatible GRU layer/weights or incompatible biases
      """
  
      def transform_kernels(kernels, func, n_gates):
          """Transforms kernel for each gate separately using given function.
  
          # Arguments
              kernels: Stacked array of kernels for individual gates.
              func: Function applied to kernel of each gate.
              n_gates: Number of gates (4 for LSTM, 3 for GRU).
          # Returns
              Stacked array of transformed kernels.
          """
          return np.hstack([func(k) for k in np.hsplit(kernels, n_gates)])
  
      def transpose_input(from_cudnn):
          """Makes a function that transforms input kernels from/to CuDNN format.
  
          It keeps the shape, but changes between the layout (Fortran/C). Eg.:
  
          ```
          Keras                 CuDNN
          [[0, 1, 2],  <--->  [[0, 2, 4],
           [3, 4, 5]]          [1, 3, 5]]
          ```
  
          It can be passed to `transform_kernels()`.
  
          # Arguments
              from_cudnn: `True` if source weights are in CuDNN format, `False`
                  if they're in plain Keras format.
          # Returns
              Function that converts input kernel to the other format.
          """
          order = 'F' if from_cudnn else 'C'
  
          def transform(kernel):
              return kernel.T.reshape(kernel.shape, order=order)
  
          return transform
  
      target_class = layer.__class__.__name__
  
      # convert the weights between CuDNNLSTM and LSTM
      if target_class in ['LSTM', 'CuDNNLSTM'] and len(weights) == 3:
          # determine if we're loading a CuDNNLSTM layer
          # from the number of bias weights:
          # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
          # if there's no bias weight in the file, skip this conversion
          units = weights[1].shape[0]
          bias_shape = weights[2].shape
          n_gates = 4
  
          if bias_shape == (2 * units * n_gates,):
              source = 'CuDNNLSTM'
          elif bias_shape == (units * n_gates,):
              source = 'LSTM'
          else:
              raise ValueError('Invalid bias shape: ' + str(bias_shape))
  
          def convert_weights(weights, from_cudnn=True):
              # transpose (and reshape) input and recurrent kernels
              kernels = transform_kernels(weights[0],
                                          transpose_input(from_cudnn),
                                          n_gates)
              recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates)
              if from_cudnn:
                  # merge input and recurrent biases into a single set
                  biases = np.sum(np.split(weights[2], 2, axis=0), axis=0)
              else:
                  # Split single set of biases evenly to two sets. The way of
                  # splitting doesn't matter as long as the two sets sum is kept.
                  biases = np.tile(0.5 * weights[2], 2)
              return [kernels, recurrent_kernels, biases]
  
          if source != target_class:
              weights = convert_weights(weights, from_cudnn=source == 'CuDNNLSTM')
  
      # convert the weights between CuDNNGRU and GRU(reset_after=True)
      if target_class in ['GRU', 'CuDNNGRU'] and len(weights) == 3:
          # We can determine the source of the weights from the shape of the bias.
          # If there is no bias we skip the conversion since CuDNNGRU always has biases.
  
          units = weights[1].shape[0]
          bias_shape = weights[2].shape
          n_gates = 3
  
          def convert_weights(weights, from_cudnn=True):
              kernels = transform_kernels(weights[0], transpose_input(from_cudnn), n_gates)
              recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates)
              biases = np.array(weights[2]).reshape((2, -1) if from_cudnn else -1)
              return [kernels, recurrent_kernels, biases]
  
          if bias_shape == (2 * units * n_gates,):
              source = 'CuDNNGRU'
          elif bias_shape == (2, units * n_gates):
              source = 'GRU(reset_after=True)'
          elif bias_shape == (units * n_gates,):
              source = 'GRU(reset_after=False)'
          else:
              raise ValueError('Invalid bias shape: ' + str(bias_shape))
  
          if target_class == 'CuDNNGRU':
              target = 'CuDNNGRU'
          elif layer.reset_after:
              target = 'GRU(reset_after=True)'
          else:
              target = 'GRU(reset_after=False)'
  
          # only convert between different types
          if source != target:
              types = (source, target)
              if 'GRU(reset_after=False)' in types:
                  raise ValueError('%s is not compatible with %s' % types)
              if source == 'CuDNNGRU':
                  weights = convert_weights(weights, from_cudnn=True)
              elif source == 'GRU(reset_after=True)':
                  weights = convert_weights(weights, from_cudnn=False)
  
      return weights
  
  
  def _need_convert_kernel(original_backend):
      """Checks if conversion on kernel matrices is required during weight loading.
  
      The convolution operation is implemented differently in different backends.
      While TH implements convolution, TF and CNTK implement the correlation operation.
      So the channel axis needs to be flipped when we're loading TF weights onto a TH model,
      or vice verca. However, there's no conversion required between TF and CNTK.
  
      # Arguments
          original_backend: Keras backend the weights were trained with, as a string.
  
      # Returns
          `True` if conversion on kernel matrices is required, otherwise `False`.
      """
      if original_backend is None:
          # backend information not available
          return False
      uses_correlation = {'tensorflow': True,
                          'theano': False,
                          'cntk': True}
      return uses_correlation[original_backend] != uses_correlation[K.backend()]
  
  
  def load_weights_from_hdf5_group(f, layers, reshape=False):
      """Implements topological (order-based) weight loading.
  
      # Arguments
          f: A pointer to a HDF5 group.
          layers: a list of target layers.
          reshape: Reshape weights to fit the layer when the correct number
              of values are present but the shape does not match.
  
      # Raises
          ValueError: in case of mismatch between provided layers
              and weights file.
      """
      if 'keras_version' in f.attrs:
          original_keras_version = f.attrs['keras_version'].decode('utf8')
      else:
          original_keras_version = '1'
      if 'backend' in f.attrs:
          original_backend = f.attrs['backend'].decode('utf8')
      else:
          original_backend = None
  
      filtered_layers = []
      for layer in layers:
          weights = layer.weights
          if weights:
              filtered_layers.append(layer)
  
      layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
      filtered_layer_names = []
      for name in layer_names:
          g = f[name]
          weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
          if weight_names:
              filtered_layer_names.append(name)
      layer_names = filtered_layer_names
      if len(layer_names) != len(filtered_layers):
          raise ValueError('You are trying to load a weight file '
                           'containing ' + str(len(layer_names)) +
                           ' layers into a model with ' +
                           str(len(filtered_layers)) + ' layers.')
  
      # We batch weight value assignments in a single backend call
      # which provides a speedup in TensorFlow.
      weight_value_tuples = []
      for k, name in enumerate(layer_names):
          g = f[name]
          weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
          weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
          layer = filtered_layers[k]
          symbolic_weights = layer.weights
          weight_values = preprocess_weights_for_loading(layer,
                                                         weight_values,
                                                         original_keras_version,
                                                         original_backend,
                                                         reshape=reshape)
          if len(weight_values) != len(symbolic_weights):
              raise ValueError('Layer #' + str(k) +
                               ' (named "' + layer.name +
                               '" in the current model) was found to '
                               'correspond to layer ' + name +
                               ' in the save file. '
                               'However the new layer ' + layer.name +
                               ' expects ' + str(len(symbolic_weights)) +
                               ' weights, but the saved weights have ' +
                               str(len(weight_values)) +
                               ' elements.')
          weight_value_tuples += zip(symbolic_weights, weight_values)
      K.batch_set_value(weight_value_tuples)
  
  
  def load_weights_from_hdf5_group_by_name(f, layers, skip_mismatch=False,
                                           reshape=False):
      """Implements name-based weight loading.
  
      (instead of topological weight loading).
  
      Layers that have no matching name are skipped.
  
      # Arguments
          f: A pointer to a HDF5 group.
          layers: A list of target layers.
          skip_mismatch: Boolean, whether to skip loading of layers
              where there is a mismatch in the number of weights,
              or a mismatch in the shape of the weights.
          reshape: Reshape weights to fit the layer when the correct number
              of values are present but the shape does not match.
  
      # Raises
          ValueError: in case of mismatch between provided layers
              and weights file and skip_mismatch=False.
      """
      if 'keras_version' in f.attrs:
          original_keras_version = f.attrs['keras_version'].decode('utf8')
      else:
          original_keras_version = '1'
      if 'backend' in f.attrs:
          original_backend = f.attrs['backend'].decode('utf8')
      else:
          original_backend = None
  
      # New file format.
      layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
  
      # Reverse index of layer name to list of layers with name.
      index = {}
      for layer in layers:
          if layer.name:
              index.setdefault(layer.name, []).append(layer)
  
      # We batch weight value assignments in a single backend call
      # which provides a speedup in TensorFlow.
      weight_value_tuples = []
      for k, name in enumerate(layer_names):
          g = f[name]
          weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
          weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
  
          for layer in index.get(name, []):
              symbolic_weights = layer.weights
              weight_values = preprocess_weights_for_loading(
                  layer,
                  weight_values,
                  original_keras_version,
                  original_backend,
                  reshape=reshape)
              if len(weight_values) != len(symbolic_weights):
                  if skip_mismatch:
                      warnings.warn('Skipping loading of weights for layer {}'.format(layer.name) +
                                    ' due to mismatch in number of weights' +
                                    ' ({} vs {}).'.format(len(symbolic_weights), len(weight_values)))
                      continue
                  else:
                      raise ValueError('Layer #' + str(k) +
                                       ' (named "' + layer.name +
                                       '") expects ' +
                                       str(len(symbolic_weights)) +
                                       ' weight(s), but the saved weights' +
                                       ' have ' + str(len(weight_values)) +
                                       ' element(s).')
              # Set values.
              for i in range(len(weight_values)):
                  if skip_mismatch:
                      if K.int_shape(symbolic_weights[i]) != weight_values[i].shape:
                          warnings.warn('Skipping loading of weights for layer {}'.format(layer.name) +
                                        ' due to mismatch in shape' +
                                        ' ({} vs {}).'.format(
                                            symbolic_weights[i].shape,
                                            weight_values[i].shape))
                          continue
  
                  weight_value_tuples.append((symbolic_weights[i],
                                              weight_values[i]))
  
      K.batch_set_value(weight_value_tuples)
  