code: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Training-related part of the Keras engine.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import weakref
  import numpy as np
  
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras import losses
  from tensorflow.python.keras import metrics as metrics_module
  from tensorflow.python.keras import optimizers
  from tensorflow.python.keras.engine import base_layer
  from tensorflow.python.keras.engine import distributed_training_utils
  from tensorflow.python.keras.engine import training_arrays
  from tensorflow.python.keras.engine import training_distributed
  from tensorflow.python.keras.engine import training_eager
  from tensorflow.python.keras.engine import training_generator
  from tensorflow.python.keras.engine import training_utils
  from tensorflow.python.keras.engine.network import Network
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import weights_broadcast_ops
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.training import optimizer as tf_optimizer_module
  from tensorflow.python.training.checkpointable import base as checkpointable
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export('keras.models.Model', 'keras.Model')
  class Model(Network):
    """`Model` groups layers into an object with training and inference features.
  
    There are two ways to instantiate a `Model`:
  
    1 - With the "functional API", where you start from `Input`,
    you chain layer calls to specify the model's forward pass,
    and finally you create your model from inputs and outputs:
  
    ```python
    import tensorflow as tf
  
    inputs = tf.keras.Input(shape=(3,))
    x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
    outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    ```
  
    2 - By subclassing the `Model` class: in that case, you should define your
    layers in `__init__` and you should implement the model's forward pass
    in `call`.
  
    ```python
    import tensorflow as tf
  
    class MyModel(tf.keras.Model):
  
      def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
  
      def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)
  
    model = MyModel()
    ```
  
    If you subclass `Model`, you can optionally have
    a `training` argument (boolean) in `call`, which you can use to specify
    a different behavior in training and inference:
  
    ```python
    import tensorflow as tf
  
    class MyModel(tf.keras.Model):
  
      def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
        self.dropout = tf.keras.layers.Dropout(0.5)
  
      def call(self, inputs, training=False):
        x = self.dense1(inputs)
        if training:
          x = self.dropout(x, training=training)
        return self.dense2(x)
  
    model = MyModel()
    ```
    """
  
    def __init__(self, *args, **kwargs):
      super(Model, self).__init__(*args, **kwargs)
      # Create a cache for iterator get_next op.
      self._iterator_get_next = weakref.WeakKeyDictionary()
      # Create a cache for dataset - uninitialized iterators
      self._dataset_iterator_cache = weakref.WeakKeyDictionary()
      # initializing _distribution_strategy here since it is possible to call
      # predict on a model without compiling it.
      self._distribution_strategy = None
  
    def _set_sample_weight_attributes(self, sample_weight_mode,
                                      skip_target_weighing_indices):
      """Sets sample weight related attributes on the model."""
      sample_weights, sample_weight_modes = training_utils.prepare_sample_weights(
          self.output_names, sample_weight_mode, skip_target_weighing_indices)
      self.sample_weights = sample_weights
      self.sample_weight_modes = sample_weight_modes
      self._feed_sample_weight_modes = [
          sample_weight_modes[i]
          for i in range(len(self.outputs))
          if i not in skip_target_weighing_indices
      ]
      self._feed_sample_weights = [
          sample_weights[i]
          for i in range(len(sample_weights))
          if i not in skip_target_weighing_indices
      ]
  
    def _get_metric_name(self, metric, output_index, weighted=False):
      """Returns the metric name corresponding to the given metric input.
  
      Arguments:
          metric: Metric function name or reference.
        output_index: Index of the current output.
          weighted: Boolean indicating if the given metric is weighted.
  
      Returns:
          A metric name.
      """
      metric_name_prefix = 'weighted_' if weighted else ''
      if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
        if metric in ('accuracy', 'acc'):
          suffix = 'acc'
        elif metric in ('crossentropy', 'ce'):
          suffix = 'ce'
      else:
        metric_fn = metrics_module.get(metric)
        # Get metric name as string
        if hasattr(metric_fn, 'name'):
          suffix = metric_fn.name
        else:
          suffix = metric_fn.__name__
      metric_name = metric_name_prefix + suffix
  
      if len(self.output_names) > 1:
        metric_name = '%s_%s' % (self.output_names[output_index], metric_name)
      j = 1
      base_metric_name = metric_name
      while metric_name in self.metrics_names:
        metric_name = '%s_%d' % (base_metric_name, j)
        j += 1
  
      return metric_name
  
    def _handle_per_output_metrics(self,
                                   metrics,
                                   y_true,
                                   y_pred,
                                   output_index,
                                   output_shape,
                                   loss_fn,
                                   mask,
                                   weights=None):
      """Calls metric functions and sets metric attributes for a single output.
  
      Arguments:
        metrics: List of metrics.
        y_true: Target output.
        y_pred: Predicted output.
        output_index: Index of the current output.
        output_shape: Shape of the current output.
        loss_fn: Loss function corresponding to the current output.
        mask: Computed mask value for the current output.
        weights: Weights to be applied on the current output.
  
      Returns:
        A list of metric result tensors.
      """
      metric_results = []
      for metric in metrics:
        metric_fn = training_utils.get_metric_function(
            metric, output_shape=output_shape, loss_fn=loss_fn)
        metric_name = self._get_metric_name(
            metric, output_index, weighted=weights is not None)
  
        with K.name_scope(metric_name):
          # If both outputs and targets are available, call the metric function.
          if y_true is not None and y_pred is not None:
            if isinstance(metric_fn, metrics_module.Metric):
              # Call the stateful metric function.
              if mask is not None:
                mask = math_ops.cast(mask, y_pred.dtype)
                # Update weights with mask.
                if weights is None:
                  weights = mask
                else:
                  # Update shape of weights if possible before adding mask.
                  # Update dimensions of weights to match with mask if possible.
                  mask, _, weights = metrics_module.squeeze_or_expand_dimensions(
                      mask, None, weights)
                  try:
                    # Broadcast weights if possible.
                    weights = weights_broadcast_ops.broadcast_weights(
                        weights, mask)
                  except ValueError:
                    pass
                    # TODO(psv): Handle case when mask and weight shapes are not
                    # compatible.
                  weights *= mask
  
              metric_result = metric_fn(y_true, y_pred, weights)
            else:
              # Call the stateless metric function.
              weighted_metric_fn = training_utils.weighted_masked_objective(
                  metric_fn)
              metric_result = weighted_metric_fn(
                  y_true, y_pred, weights=weights, mask=mask)
  
            if not context.executing_eagerly():
              # Keep track of metric result tensor.
              self.metrics_tensors.append(metric_result)
            metric_results.append(metric_result)
  
        # Keep track of metric name.
        self.metrics_names.append(metric_name)
  
        # Keep track of stateful metric attributes (name and metric function).
        if isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful:
          self.stateful_metric_names.append(metric_name)
          self.stateful_metric_functions.append(metric_fn)
          if not context.executing_eagerly():
            # Keep track of updates created by stateful metrics.
            self.metrics_updates += metric_fn.updates
      return metric_results
  
    def _handle_metrics(self,
                        outputs,
                        skip_target_indices=None,
                        targets=None,
                        sample_weights=None,
                        masks=None):
      """Handles calling metric functions and setting model metric attributes.
  
      Arguments:
        outputs: List of outputs (predictions).
        skip_target_indices: Optional. List of target ids to skip.
        targets: List of targets.
        sample_weights: Optional list of sample weight arrays.
        masks: List of computed output mask values.
  
      Returns:
        A list of metric result tensors.
      """
      skip_target_indices = skip_target_indices or []
      metric_results = []
      with K.name_scope('metrics'):
        for i in range(len(outputs)):
          if i in skip_target_indices:
            continue
          output = outputs[i] if outputs else None
          target = targets[i] if targets else None
          output_shape = None if output is None else output.get_shape().as_list()
          output_mask = masks[i] if masks else None
          metric_results.extend(
              self._handle_per_output_metrics(
                  self.nested_metrics[i], target, output, i, output_shape,
                  self.loss_functions[i], output_mask))
          metric_results.extend(
              self._handle_per_output_metrics(
                  self.nested_weighted_metrics[i],
                  target,
                  output,
                  i,
                  output_shape,
                  self.loss_functions[i],
                  output_mask,
                  weights=sample_weights[i]))
      return metric_results
  
    @checkpointable.no_automatic_dependency_tracking
    def compile(self,
                optimizer,
                loss=None,
                metrics=None,
                loss_weights=None,
                sample_weight_mode=None,
                weighted_metrics=None,
                target_tensors=None,
                distribute=None,
                **kwargs):
      """Configures the model for training.
  
      Arguments:
          optimizer: String (name of optimizer) or optimizer instance.
              See [optimizers](/api_docs/python/tf/keras/optimizers).
          loss: String (name of objective function) or objective function.
              See [losses](/api_docs/python/tf/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.
          weighted_metrics: List of metrics to be evaluated and weighted
              by sample_weight or class_weight during training and testing.
          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.
          distribute: The DistributionStrategy instance that we want to use to
              distribute the training of the model.
          **kwargs: These arguments are passed to `tf.Session.run`.
  
      Raises:
          ValueError: In case of invalid arguments for
              `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
      """
      # Validate that arguments passed by the user to `compile` are supported by
      # DistributionStrategy.
      if distribute and not isinstance(
          optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)):
        raise NotImplementedError('Only TF native optimizers are supported with '
                                  'DistributionStrategy.')
      if distribute and context.executing_eagerly():
        raise NotImplementedError('DistributionStrategy is not supported in '
                                  'Eager mode.')
      if distribute and sample_weight_mode:
        raise NotImplementedError('sample_weight_mode is not supported with '
                                  'DistributionStrategy.')
      if distribute and weighted_metrics:
        raise NotImplementedError('weighted_metrics is not supported with '
                                  'DistributionStrategy.')
      if distribute and target_tensors:
        raise ValueError('target_tensors is not supported with '
                         'DistributionStrategy.')
  
      loss = loss or {}
      if context.executing_eagerly() and not isinstance(
          optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)):
        raise ValueError('Only TF native optimizers are supported in Eager mode.')
  
      self.optimizer = optimizers.get(optimizer)
      # We've disabled automatic dependency tracking for this method, but do want
      # to add a checkpoint dependency on the optimizer if it's checkpointable.
      if isinstance(self.optimizer, checkpointable.CheckpointableBase):
        self._track_checkpointable(
            self.optimizer, name='optimizer', overwrite=True)
      self.loss = loss
      self.metrics = metrics or []
      self.loss_weights = loss_weights
      self.sample_weight_mode = sample_weight_mode
      self.weighted_metrics = weighted_metrics
      if context.executing_eagerly() and target_tensors is not None:
        raise ValueError('target_tensors is not supported in Eager mode.')
      self.target_tensors = target_tensors
  
      # Set DistributionStrategy specific parameters.
      self._distribution_strategy = distribute
      if self._distribution_strategy is not None:
        self._grouped_model = self._compile_distributed_model(
            self._distribution_strategy)
        with self._distribution_strategy.scope():
          first_replicated_model = self._distribution_strategy.unwrap(
              self._grouped_model)[0]
          # If the specified metrics in `compile` are stateful, raise an error
          # since we currently don't support stateful metrics.
          if first_replicated_model.stateful_metric_names:
            raise NotImplementedError('Stateful metrics are not supported with '
                                      'DistributionStrategy.')
  
        # We initialize the callback model with the first replicated model.
        self._replicated_model = DistributedCallbackModel(first_replicated_model)
        self._replicated_model.set_original_model(self)
      if not self.built:
        # Model is not compilable because it does not know its number of inputs
        # and outputs, nor their shapes and names. We will compile after the first
        # time the model gets called on training data.
        return
      self._is_compiled = True
  
      # 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:
            logging.warning(
                'Output "' + name + '" missing from loss dictionary. We assume '
                'this was done on purpose. The fit and evaluate APIs will not be '
                'expecting any data to be passed to "' + name + '".')
          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 = [training_utils.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.
      if not context.executing_eagerly():
        masks = [getattr(x, '_keras_mask', None) for x 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 output. '
              '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.')
      self.loss_weights_list = loss_weights_list
  
      # Initialize model metric attributes.
      self.metrics_names = ['loss']
      self.metrics_tensors = []
      self.metrics_updates = []
      self.stateful_metric_names = []
      self.stateful_metric_functions = []
  
      # Nested metrics is a list of list of metrics.
      # One list per output of the model.
      self.nested_metrics = training_utils.collect_metrics(
          metrics, self.output_names)
      self.nested_weighted_metrics = training_utils.collect_metrics(
          weighted_metrics, self.output_names)
  
      # Initialization for Eager mode execution.
      if context.executing_eagerly():
        # Prepare sample weights.
        self._set_sample_weight_attributes(sample_weight_mode,
                                           skip_target_weighing_indices)
  
        if target_tensors is not None:
          raise ValueError('target_tensors are not currently supported in Eager '
                           'mode.')
        self.total_loss = None
        for i in range(len(self.outputs)):
          if len(self.outputs) > 1:
            self.metrics_names.append(self.output_names[i] + '_loss')
  
        # Set metric attributes on model.
        self._handle_metrics(
            self.outputs,
            skip_target_indices=skip_target_indices,
            sample_weights=self.sample_weights)
  
        self.targets = []
        for i in range(len(self.outputs)):
          self._feed_output_names.append(self.output_names[i])
        self._collected_trainable_weights = self.trainable_weights
        return
  
      # Prepare targets of model.
      self.targets = []
      self._feed_targets = []
      if target_tensors not in (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 output. '
                '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))
          tmp_target_tensors = []
          for name in self.output_names:
            tmp_target_tensors.append(target_tensors.get(name, None))
          target_tensors = tmp_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 = K.int_shape(self.outputs[i])
          name = self.output_names[i]
          if target_tensors not in (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.
      self._set_sample_weight_attributes(sample_weight_mode,
                                         skip_target_weighing_indices)
  
      # 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 = self.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 ValueError('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
  
      # Invoke metric functions for all the outputs.
      self._handle_metrics(
          self.outputs,
          masks=masks,
          targets=self.targets,
          skip_target_indices=skip_target_indices,
          sample_weights=self.sample_weights)
  
      # Prepare gradient updates and state updates.
      self.total_loss = total_loss
  
      # 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 _compile_distributed_model(self, distribution_strategy):
      # TODO(anjalisridhar): Can we move the clone_and_build_model to outside the
      # model?
      def _clone_model_per_tower(model):
        new_model = training_distributed.clone_and_build_model(model)
        return new_model
  
      with distribution_strategy.scope():
        # Create a copy of this model on each of the devices.
        grouped_models = distribution_strategy.call_for_each_tower(
            _clone_model_per_tower, self)
      return grouped_models
  
    def _check_trainable_weights_consistency(self):
      """Check trainable weights count consistency.
  
      This will raise a warning if `trainable_weights` and
      `_collected_trainable_weights` are inconsistent (i.e. have different
      number of parameters).
      Inconsistency will typically arise when one modifies `model.trainable`
      without calling `model.compile` again.
      """
      if not hasattr(self, '_collected_trainable_weights'):
        return
  
      if len(self.trainable_weights) != len(self._collected_trainable_weights):
        logging.warning(
            UserWarning(
                'Discrepancy between trainable weights and collected trainable'
                ' weights, did you set `model.trainable` without calling'
                ' `model.compile` after ?'))
  
    def _make_train_function(self):
      if not hasattr(self, 'train_function'):
        raise RuntimeError('You must compile your model before using it.')
      self._check_trainable_weights_consistency()
      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
            updates = self.optimizer.get_updates(
                params=self._collected_trainable_weights, loss=self.total_loss)
          # Unconditional updates
          updates += self.get_updates_for(None)
          # Conditional updates relevant to this model
          updates += self.get_updates_for(self.inputs)
          # Stateful metrics updates
          updates += self.metrics_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 + self.metrics_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 _get_iterator_get_next_tensors(self, iterator):
      get_next_op = self._iterator_get_next.get(iterator, None)
      if get_next_op is None:
        get_next_op = iterator.get_next()
        self._iterator_get_next[iterator] = get_next_op
      return get_next_op
  
    def _distribution_standardize_user_data(self,
                                            x,
                                            y=None,
                                            sample_weight=None,
                                            class_weight=None,
                                            batch_size=None,
                                            check_steps=False,
                                            steps_name='steps',
                                            steps=None,
                                            validation_split=0):
      """Runs validation checks on input and target data passed by the user.
  
      This is called when using DistributionStrategy to train, evaluate or serve
      the model.
  
      Args:
        x: Input data. A `tf.data` dataset.
        y: Since `x` is a dataset, `y` should not be specified
          (since targets will be obtained from the iterator).
        sample_weight: An optional sample-weight array passed by the user to
          weight the importance of each sample in `x`.
        class_weight: An optional class-weight array by the user to
          weight the importance of samples in `x` based on the class they belong
          to, as conveyed by `y`.
        batch_size: Integer batch size. If provided, it is used to run additional
          validation checks on stateful models.
        check_steps: boolean, True if we want to check for validity of `steps` and
          False, otherwise.
        steps_name: The public API's parameter name for `steps`.
        steps: Integer or `None`. Total number of steps (batches of samples) to
          execute.
        validation_split: Float between 0 and 1.
          Fraction of the training data to be used as validation data.
  
      Returns:
        A tuple of 3 lists: input arrays, target arrays, sample-weight arrays.
        If the model's input and targets are symbolic, these lists are empty
        (since the model takes no user-provided data, instead the data comes
        from the symbolic inputs/targets).
  
      Raises:
        ValueError: In case of invalid user-provided data.
        RuntimeError: If the model was never compiled.
      """
      if sample_weight is not None and sample_weight.all():
        raise NotImplementedError('sample_weight is currently not supported when '
                                  'using DistributionStrategy.')
      if class_weight:
        raise NotImplementedError('class_weight is currently not supported when '
                                  'using DistributionStrategy.')
  
      # TODO(anjalisridhar): Can we use the iterator and getnext op cache?
      # We require users to pass Datasets since we distribute the dataset across
      # multiple devices.
      if not isinstance(x, dataset_ops.Dataset):
        raise ValueError('When using DistributionStrategy you must specify a '
                         'Dataset object instead of a %s.' % type(x))
      # TODO(anjalisridhar): We want distribute_dataset() to accept a Dataset or a
      # function which returns a Dataset. Currently distribute_dataset() only
      # accepts a function that returns a Dataset. Once we add support for being
      # able to clone a Dataset on multiple workers we can remove this lambda.
      result = self._distribution_strategy.distribute_dataset(lambda: x)
      iterator = result.make_initializable_iterator()
      K.get_session().run(iterator.initializer)
      # Validates `steps` argument based on x's type.
      if check_steps:
        if steps is None:
          raise ValueError('When using a Dataset instance as input to a model, '
                           'you should specify the `{steps_name}` argument.'
                           .format(steps_name=steps_name))
  
      training_utils.validate_iterator_input(x, y, sample_weight,
                                             validation_split)
      # x an y may be PerDevice objects with an input and output tensor
      # corresponding to each device. For example, x could be
      # PerDevice:{device: get_next tensor,...}.
      next_element = iterator.get_next()
  
      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 that all the elements in x and y are of the same type and shape.
      # We can then pass the first element of x and y to `_standardize_weights`
      # below and be confident of the output. We need to reopen the scope since
      # we unwrap values when we validate x and y.
      with self._distribution_strategy.scope():
        x_values, y_values = distributed_training_utils.\
          validate_distributed_dataset_inputs(self._distribution_strategy, x, y)
  
      _, _, sample_weights = self._standardize_weights(x_values,
                                                       y_values,
                                                       sample_weight,
                                                       class_weight,
                                                       batch_size)
      return x, y, sample_weights
  
    def _standardize_user_data(self,
                               x,
                               y=None,
                               sample_weight=None,
                               class_weight=None,
                               batch_size=None,
                               check_steps=False,
                               steps_name='steps',
                               steps=None,
                               validation_split=0):
      """Runs validation checks on input and target data passed by the user.
  
      Also standardizes the data to lists of arrays, in order.
  
      Also builds and compiles the model on the fly if it is a subclassed model
      that has never been called before (and thus has no inputs/outputs).
  
      This is a purely internal method, subject to refactoring at any time.
  
      Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
          - A dict mapping input names to the corresponding array/tensors,
            if the model has named inputs.
          - A `tf.data` dataset or a dataset iterator.
        y: Target data. Like the input data `x`,
          it could be either Numpy array(s) or TensorFlow tensor(s).
          It should be consistent with `x` (you cannot have Numpy inputs and
          tensor targets, or inversely). If `x` is a dataset or a
          dataset iterator, `y` should not be specified
          (since targets will be obtained from the iterator).
        sample_weight: An optional sample-weight array passed by the user to
          weight the importance of each sample in `x`.
        class_weight: An optional class-weight array by the user to
          weight the importance of samples in `x` based on the class they belong
          to, as conveyed by `y`.
        batch_size: Integer batch size. If provided, it is used to run additional
          validation checks on stateful models.
        check_steps: boolean, True if we want to check for validity of `steps` and
          False, otherwise. For example, when we are standardizing one batch of
          data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps`
          value is not required and we should not check for its validity in these
          cases.
        steps_name: The public API's parameter name for `steps`.
        steps: Integer or `None`. Total number of steps (batches of samples) to
          execute.
        validation_split: Float between 0 and 1.
          Fraction of the training data to be used as validation data.
  
      Returns:
        A tuple of 3 lists: input arrays, target arrays, sample-weight arrays.
        If the model's input and targets are symbolic, these lists are empty
        (since the model takes no user-provided data, instead the data comes
        from the symbolic inputs/targets).
  
      Raises:
        ValueError: In case of invalid user-provided data.
        RuntimeError: If the model was never compiled.
      """
      if self._distribution_strategy:
        return self._distribution_standardize_user_data(
            x,
            y,
            sample_weight=sample_weight,
            class_weight=class_weight,
            batch_size=batch_size,
            check_steps=check_steps,
            steps_name=steps_name,
            steps=steps,
            validation_split=validation_split)
  
      if isinstance(x, dataset_ops.Dataset):
        if context.executing_eagerly():
          x = x.make_one_shot_iterator()
        else:
          if x in self._dataset_iterator_cache:
            x = self._dataset_iterator_cache[x]
          else:
            iterator = x.make_initializable_iterator()
            self._dataset_iterator_cache[x] = iterator
            x = iterator
          K.get_session().run(x.initializer)
  
      # Validates `steps` argument based on x's type.
      if check_steps:
        training_utils.check_steps_argument(x, steps, steps_name)
  
      is_x_eager_iterator = isinstance(x, iterator_ops.EagerIterator)
      is_x_iterator = isinstance(x, iterator_ops.Iterator)
  
      # Validate user inputs when data is given as a dataset or dataset iterator.
      if is_x_iterator or is_x_eager_iterator:
        training_utils.validate_iterator_input(x, y, sample_weight,
                                               validation_split)
  
      # For eager iterators, when we have to process multiple batches of samples,
      # we will standardize the data when we actually loop over iterator and get
      # the batches. For now, we just return the iterator as is.
      if is_x_eager_iterator and steps is not None:
        return x, y, sample_weight
  
      # If input data is a dataset iterator in graph mode or if it is an eager
      # iterator and only one batch of samples is required, we fetch the data
      # tensors from the iterator and then standardize them.
      if is_x_iterator or is_x_eager_iterator:
        try:
          if is_x_iterator:
            next_element = self._get_iterator_get_next_tensors(x)
          else:
            next_element = x.get_next()
        except errors.OutOfRangeError:
          raise RuntimeError('Your dataset iterator ran out of data; '
                             'Make sure that your dataset can generate '
                             'required number of samples.')
  
        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
      x, y, sample_weights = self._standardize_weights(x, y, sample_weight,
                                                       class_weight, batch_size)
      return x, y, sample_weights
  
    def _standardize_weights(self, x, y, sample_weight=None, class_weight=None,
                             batch_size=None,):
      # First, we build/compile the model on the fly if necessary.
      all_inputs = []
      is_build_called = False
      is_compile_called = False
      if not self.inputs:
        # We need to use `x` to set the model inputs.
        # We type-check that `x` and `y` are either single arrays
        # or lists of arrays.
        if isinstance(x, (list, tuple)):
          if not all(isinstance(v, np.ndarray) or
                     tensor_util.is_tensor(v) for v in x):
            raise ValueError('Please provide as model inputs either a single '
                             'array or a list of arrays. You passed: x=' + str(x))
          all_inputs += list(x)
        elif isinstance(x, dict):
          raise ValueError('Please do not pass a dictionary as model inputs.')
        else:
          if not isinstance(x, np.ndarray) and not tensor_util.is_tensor(x):
            raise ValueError('Please provide as model inputs either a single '
                             'array or a list of arrays. You passed: x=' + str(x))
          all_inputs.append(x)
  
        # Build the model using the retrieved inputs (value or symbolic).
        # If values, then in symbolic-mode placeholders will be created
        # to match the value shapes.
        if not self.inputs:
          is_build_called = True
          self._set_inputs(x)
  
      if y is not None:
        if not self.optimizer:
          raise RuntimeError('You must compile a model before '
                             'training/testing. '
                             'Use `model.compile(optimizer, loss)`.')
        if not self._is_compiled:
          # On-the-fly compilation of the model.
          # We need to use `y` to set the model targets.
          if isinstance(y, (list, tuple)):
            if not all(isinstance(v, np.ndarray) or
                       tensor_util.is_tensor(v) for v in y):
              raise ValueError('Please provide as model targets either a single '
                               'array or a list of arrays. '
                               'You passed: y=' + str(y))
            all_inputs += list(y)
          elif isinstance(y, dict):
            raise ValueError('Please do not pass a dictionary as model targets.')
          else:
            if not isinstance(y, np.ndarray) and not tensor_util.is_tensor(y):
              raise ValueError('Please provide as model targets either a single '
                               'array or a list of arrays. '
                               'You passed: y=' + str(y))
            all_inputs.append(y)
  
          # Typecheck that all inputs are *either* value *or* symbolic.
          # TODO(fchollet): this check could be removed in Eager mode?
          if any(tensor_util.is_tensor(v) for v in all_inputs):
            if not all(tensor_util.is_tensor(v) for v in all_inputs):
              raise ValueError('Do not pass inputs that mix Numpy arrays and '
                               'TensorFlow tensors. '
                               'You passed: x=' + str(x) + '; y=' + str(y))
  
          if context.executing_eagerly():
            target_tensors = None
          else:
            # Handle target tensors if any passed.
            if not isinstance(y, (list, tuple)):
              y = [y]
            target_tensors = [v for v in y if tensor_util.is_tensor(v)]
          is_compile_called = True
          self.compile(optimizer=self.optimizer,
                       loss=self.loss,
                       metrics=self.metrics,
                       loss_weights=self.loss_weights,
                       target_tensors=target_tensors)
  
      # In graph mode, if we had just set inputs and targets as symbolic tensors
      # by invoking build and compile on the model respectively, we do not have to
      # feed anything to the model. Model already has input and target data as
      # part of the graph.
      # Note: in this case, `any` and `all` are equivalent since we disallow
      # mixed symbolic/value inputs.
      if (not context.executing_eagerly() and is_build_called and
          is_compile_called and
          any(tensor_util.is_tensor(v) for v in all_inputs)):
        return [], [], []
  
      # What follows is input validation and standardization to list format,
      # in the case where all inputs are value arrays.
  
      if context.executing_eagerly():
        # In eager mode, do not do shape validation
        # since the network has no input nodes (placeholders) to be fed.
        feed_input_names = self.input_names
        feed_input_shapes = None
      elif not self._is_graph_network:
        # Case: symbolic-mode subclassed network. Do not do shape validation.
        feed_input_names = self._feed_input_names
        feed_input_shapes = None
      else:
        # Case: symbolic-mode graph network.
        # In this case, we run extensive shape validation checks.
        feed_input_names = self._feed_input_names
        feed_input_shapes = self._feed_input_shapes
  
      # Standardize the inputs.
      x = training_utils.standardize_input_data(
          x,
          feed_input_names,
          feed_input_shapes,
          check_batch_axis=False,  # Don't enforce the batch size.
          exception_prefix='input')
  
      if y is not None:
        if not self._is_graph_network:
          feed_output_names = self._feed_output_names
          feed_output_shapes = None
          # Sample weighting not supported in this case.
          # TODO(fchollet): consider supporting it.
          feed_sample_weight_modes = [None for _ in self.outputs]
        else:
          feed_output_names = self._feed_output_names
          feed_sample_weight_modes = self._feed_sample_weight_modes
          feed_output_shapes = []
          for output_shape, loss_fn in zip(self._feed_output_shapes,
                                           self._feed_loss_fns):
            if loss_fn is losses.sparse_categorical_crossentropy:
              if K.image_data_format() == 'channels_first':
                feed_output_shapes.append(
                    (output_shape[0], 1) + output_shape[2:])
              else:
                feed_output_shapes.append(output_shape[:-1] + (1,))
            elif (not hasattr(loss_fn, '__name__') or
                  getattr(losses, loss_fn.__name__, None) is None):
              # If `loss_fn` is not a function (e.g. callable class)
              # or if it not in the `losses` module, then
              # it is a user-defined loss and we make no assumptions
              # about it.
              feed_output_shapes.append(None)
            else:
              feed_output_shapes.append(output_shape)
  
        # Standardize the outputs.
        y = training_utils.standardize_input_data(
            y,
            feed_output_names,
            feed_output_shapes,
            check_batch_axis=False,  # Don't enforce the batch size.
            exception_prefix='target')
  
        # Generate sample-wise weight values given the `sample_weight` and
        # `class_weight` arguments.
        sample_weights = training_utils.standardize_sample_weights(
            sample_weight, feed_output_names)
        class_weights = training_utils.standardize_class_weights(
            class_weight, feed_output_names)
        sample_weights = [
            training_utils.standardize_weights(ref, sw, cw, mode)
            for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights,
                                           feed_sample_weight_modes)
        ]
        # Check that all arrays have the same length.
        if not self._distribution_strategy:
          training_utils.check_array_lengths(x, y, sample_weights)
          if self._is_graph_network and not context.executing_eagerly():
            # Additional checks to avoid users mistakenly using improper loss fns.
            training_utils.check_loss_and_target_compatibility(
                y, self._feed_loss_fns, feed_output_shapes)
      else:
        y = []
        sample_weights = []
  
      if self.stateful and batch_size:
        # Check that for stateful networks, number of samples is a multiple
        # of the static 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
  
    @checkpointable.no_automatic_dependency_tracking
    def _set_inputs(self, inputs, training=None):
      """Set model's input and output specs based on the input data received.
  
      This is to be used for Model subclasses, which do not know at instantiation
      time what their inputs look like.
  
      Args:
        inputs: Single array, or list of arrays. The arrays could be placeholders,
          Numpy arrays, or data tensors.
          - if placeholders: the model is built on top of these placeholders,
            and we expect Numpy data to be fed for them when calling `fit`/etc.
          - if Numpy data: we create placeholders matching the shape of the Numpy
            arrays. We expect Numpy data to be fed for these placeholders
            when calling `fit`/etc.
          - if data tensors: the model is built on top of these tensors.
            We do not expect any Numpy data to be provided when calling `fit`/etc.
        training: Boolean or None. Only relevant in symbolic mode. Specifies
          whether to build the model's graph in inference mode (False), training
          mode (True), or using the Keras learning phase (None).
      """
      call_convention = getattr(
          self,
          '_call_convention',
          base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT)
      if call_convention not in (
          base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT,
          base_layer.CallConvention.SINGLE_POSITIONAL_ARGUMENT):
        raise NotImplementedError(
            'Subclassed Models without "inputs" (or single positional arguments) '
            'in their call() signatures do not yet support shape inference. File '
            'a feature request if this limitation bothers you.')
      if self.__class__.__name__ == 'Sequential':
        if tensor_util.is_tensor(inputs):
          input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:])
          self.build(input_shape=input_shape)
        else:
          input_shape = (None,) + inputs.shape[1:]
          self.build(input_shape=input_shape)
      if context.executing_eagerly():
        self._eager_set_inputs(inputs)
      else:
        self._symbolic_set_inputs(inputs, training=training)
  
    @checkpointable.no_automatic_dependency_tracking
    def _eager_set_inputs(self, inputs):
      """Set model's input and output specs based on the input data received.
  
      This is to be used for Model subclasses, which do not know at instantiation
      time what their inputs look like.
  
      We assume the number and ndim of outputs
      does not change over different calls.
  
      Args:
        inputs: Argument `x` (input data) passed by the user upon first model use.
  
      Raises:
        ValueError: If the model's inputs are already set.
      """
      assert context.executing_eagerly()
      if self.inputs:
        raise ValueError('Model inputs are already set.')
      # On-the-fly setting of model inputs/outputs as DeferredTensors,
      # to keep track of number of inputs and outputs and their ndim.
      if isinstance(inputs, (list, tuple)):
        if tensor_util.is_tensor(inputs[0]):
          dummy_output_values = self.call(
              training_utils.cast_if_floating_dtype(inputs))
        else:
          dummy_output_values = self.call(
              [ops.convert_to_tensor(v, dtype=K.floatx()) for v in inputs])
        dummy_input_values = list(inputs)
      else:
        if tensor_util.is_tensor(inputs):
          dummy_output_values = self.call(
              training_utils.cast_if_floating_dtype(inputs))
        else:
          dummy_output_values = self.call(
              ops.convert_to_tensor(inputs, dtype=K.floatx()))
        dummy_input_values = [inputs]
      if isinstance(dummy_output_values, (list, tuple)):
        dummy_output_values = list(dummy_output_values)
      else:
        dummy_output_values = [dummy_output_values]
      self.outputs = [
          base_layer.DeferredTensor(shape=(None for _ in v.shape),
                                    dtype=v.dtype) for v in dummy_output_values]
      self.inputs = [
          base_layer.DeferredTensor(shape=(None for _ in v.shape),
                                    dtype=v.dtype) for v in dummy_input_values]
      self.input_names = [
          'input_%d' % (i + 1) for i in range(len(dummy_input_values))]
      self.output_names = [
          'output_%d' % (i + 1) for i in range(len(dummy_output_values))]
      self.built = True
  
    @checkpointable.no_automatic_dependency_tracking
    def _symbolic_set_inputs(self, inputs, outputs=None, training=None):
      """Set model's inputs and output specs based.
  
      This is to be used for Model subclasses, which do not know at instantiation
      time what their inputs look like.
  
      Args:
        inputs: Argument `x` (input data) passed by the user upon first model use.
        outputs: None, a data tensor, or a list of data tensors. If None, the
          outputs will be determined by invoking self.call(), otherwise the
          provided value will be used.
        training: Boolean or None. Only relevant in symbolic mode. Specifies
          whether to build the model's graph in inference mode (False), training
          mode (True), or using the Keras learning phase (None).
  
      Raises:
        ValueError: If the model's inputs are already set.
      """
      assert not context.executing_eagerly()
      if self.inputs:
        raise ValueError('Model inputs are already set.')
  
      # On-the-fly setting of symbolic model inputs (either by using the tensor
      # provided, or by creating a placeholder if Numpy data was provided).
      self.inputs = []
      self.input_names = []
      self._feed_inputs = []
      self._feed_input_names = []
      self._feed_input_shapes = []
      if isinstance(inputs, (list, tuple)):
        inputs = list(inputs)
      else:
        inputs = [inputs]
  
      for i, v in enumerate(inputs):
        name = 'input_%d' % (i + 1)
        self.input_names.append(name)
        if isinstance(v, list):
          v = np.asarray(v)
          if v.ndim == 1:
            v = np.expand_dims(v, 1)
        if isinstance(v, (np.ndarray)):
          # We fix the placeholder shape except the batch size.
          # This is suboptimal, but it is the best we can do with the info
          # we have. The user should call `model._set_inputs(placeholders)`
          # to specify custom placeholders if the need arises.
          shape = (None,) + v.shape[1:]
          placeholder = K.placeholder(shape=shape, name=name)
          self.inputs.append(placeholder)
          self._feed_inputs.append(placeholder)
          self._feed_input_names.append(name)
          self._feed_input_shapes.append(shape)
        else:
          # Assumed tensor - TODO(fchollet) additional type check?
          self.inputs.append(v)
          if K.is_placeholder(v):
            self._feed_inputs.append(v)
            self._feed_input_names.append(name)
            self._feed_input_shapes.append(K.int_shape(v))
  
      if outputs is None:
        # Obtain symbolic outputs by calling the model.
        if len(self.inputs) == 1:
          if self._expects_training_arg:
            outputs = self.call(self.inputs[0], training=training)
          else:
            outputs = self.call(self.inputs[0])
        else:
          if self._expects_training_arg:
            outputs = self.call(self.inputs, training=training)
          else:
            outputs = self.call(self.inputs)
      if isinstance(outputs, (list, tuple)):
        outputs = list(outputs)
      else:
        outputs = [outputs]
      self.outputs = outputs
      self.output_names = [
          'output_%d' % (i + 1) for i in range(len(self.outputs))]
      self.built = True
  
    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: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset or a dataset iterator.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely). If `x` is a dataset or dataset
            iterator, `y` should not be specified
            (since targets will be obtained from the iterator).
          batch_size: Integer or `None`.
              Number of samples per gradient update.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` if your data is in the
              form of symbolic tensors, datasets, or dataset iterators
              (since they generate batches).
          epochs: Integer. Number of epochs to train the model.
              An epoch is an iteration over the entire `x` and `y`
              data provided.
              Note that in conjunction with `initial_epoch`,
              `epochs` is to be understood as "final epoch".
              The model is not trained for a number of iterations
              given by `epochs`, but merely until the epoch
              of index `epochs` is reached.
          verbose: Integer. 0, 1, or 2. Verbosity mode.
              0 = silent, 1 = progress bar, 2 = one line per epoch.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during training.
              See [callbacks](/api_docs/python/tf/keras/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.
              The validation data is selected from the last samples
              in the `x` and `y` data provided, before shuffling. This argument is
              not supported when `x` is a dataset or a dataset iterator.
          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.
              `validation_data` will override `validation_split`.
              `validation_data` could be:
                - tuple `(x_val, y_val)` of Numpy arrays or tensors
                - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays
                - dataset or a dataset iterator
          shuffle: Boolean (whether to shuffle the training data
              before each epoch) or str (for 'batch').
              'batch' is a special option for dealing with the
              limitations of HDF5 data; it shuffles in batch-sized chunks.
              Has no effect when `steps_per_epoch` is not `None`.
          class_weight: Optional dictionary mapping class indices (integers)
              to a weight (float) value, used for weighting the loss function
              (during training only).
              This can be useful to tell the model to
              "pay more attention" to samples from
              an under-represented class.
          sample_weight: Optional Numpy array of weights for
              the training samples, used for weighting the loss function
              (during training only). You can either pass a flat (1D)
              Numpy array with the same length as the input samples
              (1:1 mapping between weights and samples),
              or 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()`. This argument is not
              supported when `x` is a dataset or a dataset iterator.
          initial_epoch: Integer.
              Epoch at which to start training
              (useful for resuming a previous training run).
          steps_per_epoch: Integer or `None`.
              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 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.
          **kwargs: Used for backwards compatibility.
  
      Returns:
          A `History` object. Its `History.history` attribute is
          a record of training loss values and metrics values
          at successive epochs, as well as validation loss values
          and validation metrics values (if applicable).
  
      Raises:
          RuntimeError: If the model was never compiled.
          ValueError: In case of mismatch between the provided input data
              and what the model expects.
      """
      # TODO(fchollet): this method may be creating reference cycles, which would
      # lead to accumulating garbage in memory when called in a loop. Investigate.
  
      # Backwards compatibility
      if batch_size is None and steps_per_epoch is None:
        batch_size = 32
      # Legacy support
      if 'nb_epoch' in kwargs:
        logging.warning(
            'The `nb_epoch` argument in `fit` '
            'has been renamed `epochs`.')
        epochs = kwargs.pop('nb_epoch')
      if kwargs:
        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
  
      # Validate and standardize user data.
      if self._distribution_strategy:
        distributed_training_utils.validate_callbacks(callbacks)
  
      x, y, sample_weights = self._standardize_user_data(
          x,
          y,
          sample_weight=sample_weight,
          class_weight=class_weight,
          batch_size=batch_size,
          check_steps=True,
          steps_name='steps_per_epoch',
          steps=steps_per_epoch,
          validation_split=validation_split)
  
      # Prepare validation data.
      if validation_data:
        if (isinstance(validation_data, iterator_ops.Iterator) or
            isinstance(validation_data, iterator_ops.EagerIterator) or
            isinstance(validation_data, dataset_ops.Dataset)):
          val_x = validation_data
          val_y = None
          val_sample_weight = None
        elif len(validation_data) == 2:
          val_x, val_y = validation_data  # pylint: disable=unpacking-non-sequence
          val_sample_weight = None
        elif len(validation_data) == 3:
          val_x, val_y, val_sample_weight = validation_data  # pylint: disable=unpacking-non-sequence
        else:
          raise ValueError(
              'When passing a `validation_data` argument, '
              'it must contain either 2 items (x_val, y_val), '
              'or 3 items (x_val, y_val, val_sample_weights), '
              'or alternatively it could be a dataset or a '
              'dataset or a dataset iterator. '
              'However we received `validation_data=%s`' % validation_data)
  
        # Validate and standardize validation data.
        val_x, val_y, val_sample_weights = self._standardize_user_data(
            val_x,
            val_y,
            sample_weight=val_sample_weight,
            batch_size=batch_size,
            steps=validation_steps)
  
      elif validation_split and 0. < validation_split < 1.:
        if training_utils.has_symbolic_tensors(x):
          raise ValueError('If your data is in the form of symbolic tensors, '
                           'you cannot use `validation_split`.')
        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))
      elif validation_steps:
        val_x = []
        val_y = []
        val_sample_weights = []
      else:
        val_x = None
        val_y = None
        val_sample_weights = None
  
      if context.executing_eagerly():
        return training_eager.fit_loop(
            self,
            inputs=x,
            targets=y,
            sample_weights=sample_weights,
            class_weight=class_weight,
            batch_size=batch_size,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            val_inputs=val_x,
            val_targets=val_y,
            val_sample_weights=val_sample_weights,
            shuffle=shuffle,
            initial_epoch=initial_epoch,
            steps_per_epoch=steps_per_epoch,
            validation_steps=validation_steps)
      elif self._distribution_strategy:
        return training_distributed.fit_loop(
            self, x, y,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            val_inputs=val_x,
            val_targets=val_y,
            initial_epoch=initial_epoch,
            steps_per_epoch=steps_per_epoch,
            validation_steps=validation_steps)
      else:
        return training_arrays.fit_loop(
            self, x, y,
            sample_weights=sample_weights,
            batch_size=batch_size,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            val_inputs=val_x,
            val_targets=val_y,
            val_sample_weights=val_sample_weights,
            shuffle=shuffle,
            initial_epoch=initial_epoch,
            steps_per_epoch=steps_per_epoch,
            validation_steps=validation_steps)
  
    def evaluate(self,
                 x=None,
                 y=None,
                 batch_size=None,
                 verbose=1,
                 sample_weight=None,
                 steps=None):
      """Returns the loss value & metrics values for the model in test mode.
  
      Computation is done in batches.
  
      Arguments:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset or a dataset iterator.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely).
            If `x` is a dataset or a dataset iterator, `y` should not be specified
            (since targets will be obtained from the iterator/dataset).
          batch_size: Integer or `None`.
              Number of samples per gradient update.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` is your data is in the
              form of symbolic tensors, datasets, or dataset iterators
              (since they generate batches).
          verbose: 0 or 1. Verbosity mode.
              0 = silent, 1 = progress bar.
          sample_weight: Optional Numpy array of weights for
              the test samples, used for weighting the loss function.
              You can either pass a flat (1D)
              Numpy array with the same length as the input samples
              (1:1 mapping between weights and samples),
              or 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()`. This argument is not
              supported when `x` is a dataset or a dataset iterator.
          steps: Integer or `None`.
              Total number of steps (batches of samples)
              before declaring the evaluation round finished.
              Ignored with the default value of `None`.
  
      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 of invalid arguments.
      """
      # Backwards compatibility.
      if batch_size is None and steps is None:
        batch_size = 32
  
      # Validate and standardize user data.
      x, y, sample_weights = self._standardize_user_data(
          x,
          y,
          sample_weight=sample_weight,
          batch_size=batch_size,
          check_steps=True,
          steps_name='steps',
          steps=steps)
  
      if context.executing_eagerly():
        return training_eager.test_loop(
            self,
            inputs=x,
            targets=y,
            sample_weights=sample_weights,
            batch_size=batch_size,
            verbose=verbose,
            steps=steps)
      elif self._distribution_strategy:
        return training_distributed.test_loop(
            self,
            inputs=x,
            targets=y,
            verbose=verbose,
            steps=steps)
      else:
        return training_arrays.test_loop(
            self,
            inputs=x,
            targets=y,
            sample_weights=sample_weights,
            batch_size=batch_size,
            verbose=verbose,
            steps=steps)
  
    def predict(self, x, batch_size=None, verbose=0, steps=None):
      """Generates output predictions for the input samples.
  
      Computation is done in batches.
  
      Arguments:
           x: Input samples. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A `tf.data` dataset or a dataset iterator.
          batch_size: Integer or `None`.
              Number of samples per gradient update.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` is your data is in the
              form of symbolic tensors, dataset, or dataset iterators
              (since they generate batches).
          verbose: Verbosity mode, 0 or 1.
          steps: Total number of steps (batches of samples)
              before declaring the prediction round finished.
              Ignored with the default value of `None`.
  
      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.
      """
      # Backwards compatibility.
      if batch_size is None and steps is None:
        batch_size = 32
  
      # Validate and standardize user data.
      x, _, _ = self._standardize_user_data(
          x, check_steps=True, steps_name='steps', steps=steps)
  
      if context.executing_eagerly():
        return training_eager.predict_loop(
            self, x, batch_size=batch_size, verbose=verbose, steps=steps)
      elif self._distribution_strategy:
        return training_distributed.predict_loop(
            self, x, verbose=verbose, steps=steps)
      else:
        return training_arrays.predict_loop(
            self, x, batch_size=batch_size, verbose=verbose, steps=steps)
  
    def train_on_batch(self, x, y=None, sample_weight=None, class_weight=None):
      """Runs a single gradient update on a single batch of data.
  
      Arguments:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset or a dataset iterator.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely). If `x` is a dataset or a
            dataset iterator, `y` should not be specified
            (since targets will be obtained from the iterator).
          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(). This argument is not
              supported when `x` is a dataset or a dataset iterator.
          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.
  
      Raises:
        ValueError: In case of invalid user-provided arguments.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`train_on_batch` is not supported for models '
                                  'compiled with DistributionStrategy.')
      # Validate and standardize user data.
      x, y, sample_weights = self._standardize_user_data(
          x, y, sample_weight=sample_weight, class_weight=class_weight)
  
      if context.executing_eagerly():
        outputs = training_eager.train_on_batch(
            self, x, y, sample_weights=sample_weights)
      else:
        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=None, sample_weight=None):
      """Test the model on a single batch of samples.
  
      Arguments:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset or a dataset iterator.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely). If `x` is a dataset or a
            dataset iterator, `y` should not be specified
            (since targets will be obtained from the iterator).
          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(). This argument is not
              supported when `x` is a dataset or a dataset iterator.
  
      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 of invalid user-provided arguments.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`test_on_batch` is not supported for models '
                                  'compiled with DistributionStrategy.')
      # Validate and standardize user data.
      x, y, sample_weights = self._standardize_user_data(
          x, y, sample_weight=sample_weight)
  
      if context.executing_eagerly():
        outputs = training_eager.test_on_batch(
            self, x, y, sample_weights=sample_weights)
      else:
        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 data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A `tf.data` dataset or a dataset iterator.
  
      Returns:
          Numpy array(s) of predictions.
  
      Raises:
          ValueError: In case of mismatch between given number of inputs and
            expectations of the model.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`predict_on_batch` is not supported for '
                                  'models compiled with DistributionStrategy.')
      # Validate and standardize user data.
      inputs, _, _ = self._standardize_user_data(x)
      if context.executing_eagerly():
        if (isinstance(x, iterator_ops.EagerIterator) or
            (isinstance(x, dataset_ops.Dataset) and context.executing_eagerly())):
          inputs = training_utils.cast_if_floating_dtype(inputs)
        else:
          inputs = [
              ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs
          ]
        return self(inputs)  # pylint: disable=not-callable
  
      if not context.executing_eagerly():
        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
          ins = inputs + [0]
        else:
          ins = inputs
  
        self._make_predict_function()
        outputs = self.predict_function(ins)
  
        if len(outputs) == 1:
          return outputs[0]
        return outputs
  
    def fit_generator(self,
                      generator,
                      steps_per_epoch=None,
                      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)`.
              This tuple (a single output of the generator) makes a single batch.
              Therefore, all arrays in this tuple must have the same length (equal
              to the size of this batch). Different batches may have different
                sizes.
              For example, the last batch of the epoch is commonly smaller than
                the
              others, if the size of the dataset is not divisible by the batch
                size.
              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 samples of your dataset
              divided by the batch size.
              Optional for `Sequence`: if unspecified, will use
              the `len(generator)` as a number of steps.
          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.
              Optional for `Sequence`: if unspecified, will use
              the `len(validation_data)` as a number of steps.
          class_weight: Dictionary mapping class indices to a weight
              for the class.
          max_queue_size: Integer. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Maximum number of processes to spin up
              when using process-based threading.
              If unspecified, `workers` will default to 1. If 0, will
              execute the generator on the main thread.
          use_multiprocessing: Boolean.
              If `True`, use process-based threading.
              If unspecified, `use_multiprocessing` will default to `False`.
              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: Boolean. Whether to shuffle the order of the batches at
              the beginning of each epoch. Only used with instances
              of `Sequence` (`keras.utils.Sequence`).
              Has no effect when `steps_per_epoch` is not `None`.
          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.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`fit_generator` is not supported for '
                                  'models compiled with DistributionStrategy.')
  
      if not self.built and not self._is_graph_network:
        raise NotImplementedError(
            '`fit_generator` is not yet enabled for unbuilt Model subclasses')
  
      return training_generator.fit_generator(
          self,
          generator,
          steps_per_epoch=steps_per_epoch,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          validation_data=validation_data,
          validation_steps=validation_steps,
          class_weight=class_weight,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          shuffle=shuffle,
          initial_epoch=initial_epoch)
  
    def evaluate_generator(self,
                           generator,
                           steps=None,
                           max_queue_size=10,
                           workers=1,
                           use_multiprocessing=False,
                           verbose=0):
      """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.
              Optional for `Sequence`: if unspecified, will use
              the `len(generator)` as a number of steps.
          max_queue_size: maximum size for the generator queue
          workers: Integer. Maximum number of processes to spin up
              when using process-based threading.
              If unspecified, `workers` will default to 1. If 0, will
              execute the generator on the main thread.
          use_multiprocessing: Boolean.
              If `True`, use process-based threading.
              If unspecified, `use_multiprocessing` will default to `False`.
              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:
          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 of invalid arguments.
  
      Raises:
          ValueError: In case the generator yields data in an invalid format.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`evaluate_generator` is not supported for '
                                  'models compiled with DistributionStrategy.')
  
      if not self.built and not self._is_graph_network:
        raise NotImplementedError(
            '`evaluate_generator` is not yet enabled for '
            'unbuilt Model subclasses')
  
      return training_generator.evaluate_generator(
          self,
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose)
  
    def predict_generator(self,
                          generator,
                          steps=None,
                          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.
              Optional for `Sequence`: if unspecified, will use
              the `len(generator)` as a number of steps.
          max_queue_size: Maximum size for the generator queue.
          workers: Integer. Maximum number of processes to spin up
              when using process-based threading.
              If unspecified, `workers` will default to 1. If 0, will
              execute the generator on the main thread.
          use_multiprocessing: Boolean.
              If `True`, use process-based threading.
              If unspecified, `use_multiprocessing` will default to `False`.
              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.
      """
      if self._distribution_strategy:
        raise NotImplementedError('`predict_generator` is not supported for '
                                  'models compiled with DistributionStrategy.')
  
      if not self.built and not self._is_graph_network:
        raise NotImplementedError(
            '`predict_generator` is not yet enabled for unbuilt Model subclasses')
  
      return training_generator.predict_generator(
          self,
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose)
  
    def _get_callback_model(self):
      """Returns the Callback Model for this Model."""
  
      if hasattr(self, '_replicated_model') and self._replicated_model:
        # When using training_distributed, we set the callback model
        # to an instance of the `DistributedModel` that we create in
        # the `compile` call. The `DistributedModel` is initialized
        # with the first replicated model. We need to set the callback
        # model to a DistributedModel to allow us to override saving
        # and loading weights when we checkpoint the model during training.
        return self._replicated_model
      if hasattr(self, 'callback_model') and self.callback_model:
        return self.callback_model
      return self
  
  
  class DistributedCallbackModel(Model):
    """Model that is used for callbacks with DistributionStrategy."""
  
    def __init__(self, model):
      super(DistributedCallbackModel, self).__init__()
      # TODO(anjalisridhar): Right now the only attributes set are the layer and
      # weights. We may need to set additional attributes as needed since we have
      # not called compile on this model.
  
    def set_original_model(self, orig_model):
      self._original_model = orig_model
  
    def save_weights(self, filepath, overwrite=True, save_format=None):
      self._replicated_model.save_weights(filepath, overwrite=overwrite,
                                          save_format=save_format)
  
    def save(self, filepath, overwrite=True, include_optimizer=True):
      # save weights from the distributed model to the original model
      distributed_model_weights = self.get_weights()
      self._original_model.set_weights(distributed_model_weights)
      # TODO(anjalisridhar): Do we need to save the original model here?
      # Saving the first replicated model works as well.
      self._original_model.save(filepath, overwrite=True, include_optimizer=False)
  
    def load_weights(self, filepath, by_name=False):
      self._original_model.load_weights(filepath, by_name=False)
      # Copy the weights from the original model to each of the replicated models.
      orig_model_weights = self._original_model.get_weights()
      distributed_training_utils.set_weights(
          self._original_model._distribution_strategy, self,  # pylint: disable=protected-access
          orig_model_weights)
  
    def __getattr__(self, item):
      # Whitelisted atttributes of the model that can be accessed by the user
      # during a callback.
      if item not in ['_setattr_tracking']:
        logging.warning('You are accessing attribute ' + item + 'of the'
                        'DistributedCallbackModel that may not have been set'
                        'correctly.')
  
apis: tensorflow.python.keras.engine.training_arrays.test_loop tensorflow.python.keras.losses.get tensorflow.python.keras.engine.training_utils.get_metric_function tensorflow.python.keras.engine.training_utils.collect_metrics tensorflow.python.keras.engine.training_utils.standardize_class_weights tensorflow.python.keras.engine.training_eager.train_on_batch tensorflow.python.keras.engine.training_eager.fit_loop numpy.asarray tensorflow.python.keras.engine.training_utils.check_loss_and_target_compatibility tensorflow.python.keras.backend.image_data_format tensorflow.python.keras.engine.training_arrays.predict_loop tensorflow.python.keras.engine.training_utils.has_symbolic_tensors tensorflow.python.keras.engine.training_utils.cast_if_floating_dtype tensorflow.python.keras.engine.training_distributed.fit_loop tensorflow.python.keras.engine.training_eager.test_loop tensorflow.python.keras.backend.learning_phase tensorflow.python.keras.backend.function tensorflow.python.util.tf_export.tf_export tensorflow.python.keras.engine.base_layer.DeferredTensor tensorflow.python.keras.backend.placeholder tensorflow.python.keras.engine.training_utils.weighted_masked_objective tensorflow.python.keras.engine.distributed_training_utils.set_weights tensorflow.python.keras.optimizers.get tensorflow.python.keras.engine.distributed_training_utils.validate_distributed_dataset_inputs tensorflow.python.keras.engine.training_arrays.fit_loop numpy.expand_dims tensorflow.python.keras.backend.int_shape tensorflow.python.keras.engine.training_utils.prepare_sample_weights tensorflow.python.keras.engine.training_utils.standardize_sample_weights tensorflow.python.platform.tf_logging.warning tensorflow.python.keras.engine.training_distributed.predict_loop tensorflow.python.keras.backend.name_scope tensorflow.python.keras.engine.training_eager.test_on_batch tensorflow.python.keras.engine.training_utils.validate_iterator_input tensorflow.python.keras.backend.dtype tensorflow.python.keras.backend.is_placeholder tensorflow.python.keras.utils.generic_utils.slice_arrays tensorflow.python.ops.math_ops.cast tensorflow.python.keras.engine.training_eager.predict_loop tensorflow.python.keras.engine.training_generator.evaluate_generator tensorflow.python.keras.backend.floatx tensorflow.python.keras.engine.training_generator.fit_generator tensorflow.python.keras.metrics.get tensorflow.python.keras.engine.training_generator.predict_generator tensorflow.python.keras.engine.training_utils.standardize_weights tensorflow.python.keras.engine.training_distributed.test_loop tensorflow.python.keras.engine.distributed_training_utils.validate_callbacks tensorflow.python.keras.metrics.squeeze_or_expand_dimensions tensorflow.python.keras.engine.training_utils.check_steps_argument tensorflow.python.ops.weights_broadcast_ops.broadcast_weights tensorflow.python.keras.backend.get_session tensorflow.python.eager.context.executing_eagerly tensorflow.python.keras.engine.training_distributed.clone_and_build_model tensorflow.python.keras.backend.is_sparse tensorflow.python.keras.engine.training_utils.standardize_input_data tensorflow.python.keras.engine.training_utils.check_array_lengths tensorflow.python.framework.tensor_util.is_tensor