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 = math_ops.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(
          math_ops.cast(math_ops.not_equal(weights, 0), K.floatx()))
    return K.mean(score_array)

def logcosh(y_true, y_pred):
  """Logarithm of the hyperbolic cosine of the prediction error.

  `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
  to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
  like the mean squared error, but will not be so strongly affected by the
  occasional wildly incorrect prediction.

  Arguments:
      y_true: tensor of true targets.
      y_pred: tensor of predicted targets.

  Returns:
      Tensor with one scalar loss entry per sample.
  """

  def _logcosh(x):
    return x + nn.softplus(-2. * x) - math_ops.log(2.)

  return K.mean(_logcosh(y_pred - y_true), axis=-1)

def _eager_metrics_fn(model, outputs, targets):
  """Calculates the metrics for each output of the given model.

  Arguments:
      model: The model on which metrics are being calculated.
      outputs: The outputs of the given model.
      targets: The predictions or targets of the given model.

  Returns:
      Returns the metric names and metric results for each output of the model.
  """
  metric_names = []
  metric_results = []
  if not isinstance(outputs, list):
    outputs = [outputs]

  if not isinstance(targets, list):
    targets = [targets]

  for i in range(len(model.outputs)):
    output_metrics = model.nested_metrics[i]
    for nested_output_metric in output_metrics:
      metric_name, metric_fn = _get_metrics_info(
          nested_output_metric, backend.int_shape(model.outputs[i]),
          model.loss_functions[i])

      if len(model.output_names) > 1:
        metric_name = model.output_names[i] + '_' + metric_name
        if metric_name not in model.metrics_names:
          model.metrics_names.append(metric_name)

      with backend.name_scope(metric_name):
        metric_result = metric_fn(outputs[i], targets[i])
        metric_names.append(metric_name)
        metric_results.append(backend.mean(metric_result))

  return metric_results

def _model_loss(model, inputs, targets):
  """Calculates the loss for a given model.

  Arguments:
     model: The model on which metrics are being calculated.
     inputs: The inputs of the given model. This is typically the mini batch of
              data that is fed to the model.
     targets: The predictions or targets of the given model.

  Returns:
     Returns the model output, total loss and loss value calculated using the
     specified loss function. The total loss includes regularization losses and
     applies masking and sample weighting to the loss value.
  """
  total_loss = 0
  if len(inputs) == 1:
    outs = model.call(inputs[0])
  else:
    outs = model.call(inputs)
  if not isinstance(outs, list):
    outs = [outs]

  if not isinstance(targets, list):
    targets = [targets]

  loss_metrics = []
  with K.name_scope('loss'):
    for i, loss_fn in enumerate(model.loss_functions):
      # compute the loss
      output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn,
                                   model.output_names[i])
      loss_metrics.append(K.mean(output_loss))

      mask = outs[i]._keras_mask
      # adapted from weighted_loss_fn
      if mask is not None:
        # mask should have the same shape as output_loss
        output_loss *= mask
        #  the loss per batch should be proportional
        #  to the number of unmasked samples.
        output_loss /= K.mean(mask)

      # adapted from weighted_loss_fn
      # apply sample weighting
      if model.sample_weights:
        # reduce score_array to same ndim as weight array
        ndim = K.ndim(output_loss)
        weight_ndim = K.ndim(model.sample_weights)
        output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim)))
        output_loss *= model.sample_weights
        output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0),
                                     K.floatx()))
        output_loss = K.mean(output_loss)

      loss_weight = model.loss_weights_list[i]
      if total_loss is None:
        total_loss = loss_weight * output_loss
      else:
        total_loss += loss_weight * output_loss

    total_loss = K.mean(total_loss)
    # Add regularization losses
    custom_losses = []
    for layer in model.layers:
      if layer.losses:
        custom_losses += layer.losses

    if custom_losses:
      total_loss += sum(custom_losses)

  return outs, total_loss, loss_metrics

def top_k_categorical_accuracy(y_true, y_pred, k=5):
  return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1)

 def call(self, inputs):
   if self.data_format == 'channels_last':
     return K.mean(inputs, axis=[1, 2, 3])
   else:
     return K.mean(inputs, axis=[2, 3, 4])

def squared_hinge(y_true, y_pred):
  return K.mean(
      math_ops.square(math_ops.maximum(1. - y_true * y_pred, 0.)), axis=-1)

def mean_absolute_percentage_error(y_true, y_pred):
  diff = math_ops.abs(
      (y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
  return 100. * K.mean(diff, axis=-1)

def mean_squared_error(y_true, y_pred):
  return K.mean(math_ops.square(y_pred - y_true), axis=-1)

def batch_test_loop(model,
                    inputs,
                    targets,
                    batch_size,
                    sample_weights=None,
                    verbose=0):
  """Test function for eager execution when input is given as arrays or tensors.

  Arguments:
      model: Model instance that is being evaluated in Eager mode.
      inputs: List of input arrays.
      targets: List of target arrays.
      batch_size: Integer batch size.
      sample_weights: Optional list of sample weight arrays.
      verbose: Verbosity mode.

  Returns:
      Scalar loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.
  """
  outs = []
  feed_data = inputs + targets
  if sample_weights:
    feed_data += sample_weights
  num_samples = training_utils.check_num_samples(
      feed_data, batch_size=batch_size)
  if verbose == 1:
    progbar = generic_utils.Progbar(target=num_samples)
  batches = generic_utils.make_batches(num_samples, batch_size)
  index_array = np.arange(num_samples)
  for batch_index, (batch_start, batch_end) in enumerate(batches):
    batch_ids = index_array[batch_start:batch_end]
    inputs_batch = slice_arrays(inputs, batch_ids)
    targets_batch = slice_arrays(targets, batch_ids)
    if sample_weights:
      sample_weights_batch = slice_arrays(sample_weights, batch_ids)
    else:
      sample_weights_batch = None

    inputs_batch = [
        ops.convert_to_tensor(val, dtype=backend.floatx())
        for val in inputs_batch
    ]
    targets_batch = [
        ops.convert_to_tensor(val, dtype=backend.floatx())
        for val in targets_batch
    ]
    if sample_weights:
      sample_weights_batch = [
          ops.convert_to_tensor(val, dtype=backend.floatx())
          if val is not None else None for val in sample_weights_batch
      ]

    loss_outs, loss, loss_metrics = _model_loss(
        model,
        inputs_batch,
        targets_batch,
        sample_weights=sample_weights_batch,
        training=False)
    metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
    batch_outs = []
    for _, v in zip(model.metrics_names,
                    [backend.mean(loss)] + loss_metrics + metrics_results):
      batch_outs.append(tensor_util.constant_value(v))

    if isinstance(batch_outs, list):
      if batch_index == 0:
        for _ 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] /= num_samples
  if len(outs) == 1:
    return outs[0]
  return outs

def iterator_test_loop(model, inputs, steps, verbose=0):
  """Test function for eager execution when input is given as dataset iterator.

  Arguments:
      model: Model instance that is being evaluated in Eager mode.
      inputs: Input dataset iterator.
      steps: Total number of steps (batches of samples) before declaring
      predictions finished.
      verbose: Verbosity mode.

  Returns:
      Scalar loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.

  Raises:
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  assert isinstance(inputs, iterator_ops.EagerIterator)
  outs = []
  num_samples = 0
  if verbose == 1:
    progbar = generic_utils.Progbar(target=steps)
  for step_index in range(steps):
    # Get data from the iterator.
    try:
      next_element = inputs.get_next()
    except errors.OutOfRangeError:
      logging.warning(
          'Your dataset iterator ran out of data interrupting testing. '
          'Make sure that your dataset can generate at least `steps` batches '
          '(in this case, %d batches).', steps)
      break

    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
      raise ValueError('Please provide data as a list or tuple of 2 elements '
                       ' - input and target pair. Received %s' % next_element)
    x, y = next_element

    # Validate and standardize data.
    x, y, sample_weights = model._standardize_user_data(x, y)

    # Calculate model output, loss values.
    loss_outs, loss, loss_metrics = _model_loss(
        model, x, y, sample_weights=sample_weights, training=False)
    metrics_results = _eager_metrics_fn(model, loss_outs, y)
    batch_outs = []
    for _, v in zip(model.metrics_names,
                    [backend.mean(loss)] + loss_metrics + metrics_results):
      batch_outs.append(tensor_util.constant_value(v))

    # Get current step size.
    if isinstance(x, list):
      step_size = x[0].get_shape().as_list()[0]
    else:
      step_size = x.get_shape().as_list()[0]

    # Accumulate results in output array.
    if not isinstance(batch_outs, list):
      batch_outs = [batch_outs]
    if step_index == 0:
      for _ in enumerate(batch_outs):
        outs.append(0.)
    for i, batch_out in enumerate(batch_outs):
      outs[i] += batch_out * step_size

    # Calculate sample size.
    num_samples += step_size
    if verbose == 1:
      progbar.update(step_index + 1)

    for i in range(len(outs)):
      outs[i] /= num_samples
    if len(outs) == 1:
      return outs[0]
    return outs

def batch_fit_loop(model,
                   inputs,
                   targets,
                   epoch_logs,
                   index_array,
                   out_labels,
                   callback_model,
                   batch_size,
                   sample_weights=None,
                   val_inputs=None,
                   val_targets=None,
                   val_sample_weights=None,
                   callbacks=None,
                   shuffle=True,
                   num_train_samples=None,
                   do_validation=False):
  """Fit function for eager execution when input is given as arrays or tensors.

  Updates the given epoch logs.

  Arguments:
      model: Instance of the `Model`.
      inputs: List of input arrays.
      targets: List of target arrays.
      epoch_logs: Dictionary of logs from every epoch.
      index_array: Index array generated from number of training samples.
      out_labels: Output labels generated from model metric names.
      callback_model: Instance of `Model` to callback.
      batch_size: Integer batch size or None if unknown.
      sample_weights: Optional list of sample weight arrays.
      val_inputs: Input data for validation.
      val_targets: Target data for validation.
      val_sample_weights: Sample weight data for validation.
      callbacks: List of callbacks to be called during training.
      shuffle: Whether to shuffle the data at the beginning of each epoch.
      num_train_samples: Integer number of training samples.
      do_validation: Boolean value indicating whether we should do validation.
  """
  # TODO(psv): Create a dataset iterator instead of manually creating batches
  # here and in batch_test_loop, batch_predict_loop.
  if shuffle == 'batch':
    index_array = model._batch_shuffle(index_array, batch_size)
  elif shuffle:
    np.random.shuffle(index_array)

  batches = generic_utils.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]
    inputs_batch = slice_arrays(inputs, batch_ids, contiguous=not shuffle)
    targets_batch = slice_arrays(targets, batch_ids, contiguous=not shuffle)
    if sample_weights:
      sample_weights_batch = slice_arrays(
          sample_weights, batch_ids, contiguous=not shuffle)
    else:
      sample_weights_batch = None
    batch_logs = {}
    batch_logs['batch'] = batch_index
    batch_logs['size'] = len(batch_ids)

    callbacks.on_batch_begin(batch_index, batch_logs)

    inputs_batch = [
        ops.convert_to_tensor(val, dtype=backend.floatx())
        for val in inputs_batch
    ]
    targets_batch = [
        ops.convert_to_tensor(val, dtype=backend.floatx())
        for val in targets_batch
    ]
    if sample_weights:
      sample_weights_batch = [
          ops.convert_to_tensor(val, dtype=backend.floatx())
          if val is not None else None for val in sample_weights_batch
      ]

    outs, loss, loss_metrics = _process_single_batch(
        model,
        inputs_batch,
        targets_batch,
        sample_weights=sample_weights_batch,
        training=True)

    if not isinstance(outs, list):
      outs = [outs]

    for l, o in zip(out_labels, outs):
      batch_logs[l] = o
    # Required for eager execution
    metrics_results = _eager_metrics_fn(model, outs, targets_batch)
    batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))

    for k, v in zip(model.metrics_names,
                    [backend.mean(loss)] + loss_metrics + metrics_results):
      batch_logs[k] = tensor_util.constant_value(v)
    callbacks.on_batch_end(batch_index, batch_logs)
    if callback_model.stop_training:
      break

    if batch_index == len(batches) - 1:  # Last batch.
#.........这里部分代码省略.........

#.........这里部分代码省略.........
      val_inputs: Input data for validation.
      val_targets: Target data for validation.
      val_sample_weights: Sample weight data for validation.
      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
      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`.
      validation_steps: Number of steps to run validation for (only if doing
        validation from data tensors). Ignored with default value of `None`.
      do_validation: Boolean value indicating whether we should do validation.

  Raises:
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  assert isinstance(inputs, iterator_ops.EagerIterator)
  for step_index in range(steps_per_epoch):
    batch_logs = {}
    batch_logs['batch'] = step_index
    batch_logs['size'] = 1
    callbacks.on_batch_begin(step_index, batch_logs)

    # Get data from the iterator.
    try:
      next_element = inputs.get_next()
    except errors.OutOfRangeError:
      logging.warning(
          'Your dataset iterator ran out of data; '
          'interrupting training. Make sure that your dataset'
          ' can generate at least `steps_per_epoch * epochs` '
          'batches (in this case, %d batches).' % steps_per_epoch * epochs)
      break

    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
      raise ValueError('Please provide data as a list or tuple of 2 elements '
                       ' - input and target pair. Received %s' % next_element)
    x, y = next_element

    # Validate and standardize data.
    x, y, sample_weights = model._standardize_user_data(
        x, y, class_weight=class_weight)
    if sample_weights:
      sample_weights = [
          ops.convert_to_tensor(val, dtype=backend.floatx())
          if val is not None else None for val in sample_weights
      ]

    if step_index == 0 and not callback_metrics:
      out_labels = model.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)
      callbacks.set_params({
          'epochs': epochs,
          'steps': steps_per_epoch,
          'verbose': verbose,
          'do_validation': do_validation,
          'metrics': callback_metrics or [],
      })

    # Train model.
    outs, loss, loss_metrics = _process_single_batch(
        model, x, y, sample_weights=sample_weights, training=True)
    if not isinstance(outs, list):
      outs = [outs]

    # Calculate metrics.
    for l, o in zip(out_labels, outs):
      batch_logs[l] = o
    # Required for eager execution
    metrics_results = _eager_metrics_fn(model, outs, y)
    batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))

    for k, v in zip(model.metrics_names,
                    [backend.mean(loss)] + loss_metrics + metrics_results):
      batch_logs[k] = tensor_util.constant_value(v)
    callbacks.on_batch_end(step_index, batch_logs)
    if callback_model.stop_training:
      break

    if step_index == steps_per_epoch - 1:
      if do_validation:
        val_outs = test_loop(
            model,
            val_inputs,
            val_targets,
            sample_weights=val_sample_weights,
            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

def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
  """Abstract method to loop over some data in batches.

  Arguments:
      model: Model instance that is being evaluated in Eager mode.
      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.
  """
  K.set_learning_phase(False)
  num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
  outs = []
  if verbose == 1:
    progbar = Progbar(target=num_samples)
  batches = make_batches(num_samples, batch_size)
  index_array = np.arange(num_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)

    ins_batch_converted = []
    for ib in ins_batch:
      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))

    eager_model_inputs = []
    eager_model_outputs = []
    for i in range(len(model.inputs)):
      eager_model_inputs.append(ins_batch_converted[i])

    for i in range(len(model.inputs), len(ins_batch_converted)):
      eager_model_outputs.append(ins_batch_converted[i])

    loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
                                                eager_model_outputs)
    _, metrics_results = _eager_metrics_fn(model, loss_outs,
                                           eager_model_outputs)
    batch_outs = []
    for _, v in zip(model.metrics_names,
                    [K.mean(loss)] + loss_metrics + metrics_results):
      batch_outs.append(tensor_util.constant_value(v))

    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] /= num_samples
  if len(outs) == 1:
    return outs[0]
  return outs

#.........这里部分代码省略.........
  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 shuffle == 'batch':
      index_array = model._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)

      ins_batch_converted = []
      for ib in ins_batch:
        ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
      eager_model_inputs = []
      eager_model_outputs = []
      for i in range(len(model.inputs)):
        eager_model_inputs.append(ins_batch_converted[i])

      for i in range(len(model.inputs), len(ins_batch_converted)):
        eager_model_outputs.append(ins_batch_converted[i])

      outs, loss, loss_metrics = _process_single_batch(eager_model_inputs,
                                                       eager_model_outputs,
                                                       model)

      if not isinstance(outs, list):
        outs = [outs]

      for l, o in zip(out_labels, outs):
        batch_logs[l] = o
      # Required for Eager mode
      metrics_names, metrics_results = _eager_metrics_fn(model, outs,
                                                         eager_model_outputs)
      batch_logs['loss'] = tensor_util.constant_value(K.mean(loss))

      # TODO(anjalisridhar): Move this to compile to avoid duplicate code.
      # In graph mode we set the metric names in compile. However in
      # Eager mode we calculate the metrics for each batch in fit_loop.
      # We could calculate the metric names and functions in compile.
      # This would avoid setting the callback parameters separately.
      # We need to do this for the first iteration alone
      for m in metrics_names:
        if m not in callback_metrics:
          callback_metrics.append(m)

      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 [],
      })

      for k, v in zip(model.metrics_names,
                      [K.mean(loss)] + loss_metrics + metrics_results):
        batch_logs[k] = tensor_util.constant_value(v)

      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 = test_loop(
              model, 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 model.history

def binary_crossentropy(y_true, y_pred):
  return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)

def poisson(y_true, y_pred):
  return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)

def logcosh(y_true, y_pred):

  def _logcosh(x):
    return x + K.softplus(-2. * x) - K.log(2.)

  return K.mean(_logcosh(y_pred - y_true), axis=-1)

def mean_absolute_error(y_true, y_pred):
  return K.mean(math_ops.abs(y_pred - y_true), axis=-1)

def mean_absolute_percentage_error(y_true, y_pred):
  # Equivalent to MAE, but sometimes easier to interpret.
  diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None))
  return 100. * K.mean(diff, axis=-1)