text: # 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 collections
  import weakref
  import numpy as np
  
  from tensorflow.python import tf2
  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_shape
  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 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.optimizer_v2 import optimizer_v2
  from tensorflow.python.keras.utils import data_utils
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  from tensorflow.python.keras.utils.losses_utils import squeeze_or_expand_dimensions
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.training import distribution_strategy_context
  from tensorflow.python.training import optimizer as tf_optimizer_module
  from tensorflow.python.training.checkpointable import base as checkpointable
  from tensorflow.python.training.mode_keys import ModeKeys
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_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
      # This flag is used to track if the user is using the deprecated path of
      # passing distribution strategy to compile rather than creating the model
      # under distribution strategy scope.
      self._compile_distribution = False
  
      self.run_eagerly = None
  
    def get_weights(self):
      """Retrieves the weights of the model.
  
      Returns:
          A flat list of Numpy arrays.
      """
      if self._distribution_strategy:
        with self._distribution_strategy.scope():
          return super(Model, self).get_weights()
      return super(Model, self).get_weights()
  
    @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 `tf.keras.optimizers`.
          loss: String (name of objective function) or objective function.
              See `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: NOT SUPPORTED IN TF 2.0, please create and compile the
              model under distribution strategy scope instead of passing it to
              compile.
          **kwargs: Any additional arguments.
  
      Raises:
          ValueError: In case of invalid arguments for
              `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
      """
      run_eagerly = kwargs.pop('run_eagerly', None)
      self._run_eagerly = run_eagerly
      optimizer = optimizers.get(optimizer)
  
      if distribute is not None:
        if tf2.enabled():
          raise ValueError(
              'Distribute argument in compile is not available in TF 2.0 please '
              'create the model under the distribution strategy scope.')
        logging.warning('Distribute argument in compile is deprecated please '
                        'create the model under the distribution strategy scope.')
        self._distribution_strategy = distribute
        self._compile_distribution = True
      else:
        if distribution_strategy_context.has_distribution_strategy():
          # When the user builds the model in the DS scope and cross replica
          # context we want distribution strategy to be set but when building the
          # replica copies of the models internally we should not be compiling
          # with distribution strategy and use the default compilation path.
          if distribution_strategy_context.in_cross_replica_context():
            self._distribution_strategy = (
                distribution_strategy_context.get_distribution_strategy())
  
      # Validate that arguments passed by the user to `compile` are supported by
      # DistributionStrategy.
      if self._distribution_strategy:
        if not isinstance(optimizer,
                          (tf_optimizer_module.Optimizer, optimizers.TFOptimizer,
                           optimizer_v2.OptimizerV2)):
          raise NotImplementedError(
              'optimizer must be an instance of '
              'tf.train.Optimizer, not a %s' % type(optimizer))
        if sample_weight_mode:
          raise NotImplementedError('sample_weight_mode is not supported with '
                                    'DistributionStrategy.')
        if weighted_metrics:
          raise NotImplementedError('weighted_metrics is not supported with '
                                    'DistributionStrategy.')
        if target_tensors:
          raise ValueError('target_tensors is not supported with '
                           'DistributionStrategy.')
  
      loss = loss or {}
      if self.run_eagerly and not isinstance(
          optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer,
                      optimizer_v2.OptimizerV2)):
        raise ValueError(
            'When running a model in eager execution, the optimizer must be an '
            'instance of tf.train.Optimizer. Received: '
            '%s' % optimizer)
  
      self.optimizer = 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._compile_metrics = metrics or []
      self.loss_weights = loss_weights
      self.sample_weight_mode = sample_weight_mode
      self._compile_weighted_metrics = weighted_metrics
      if self.run_eagerly and target_tensors is not None:
        raise ValueError(
            'target_tensors argument is not supported when '
            'running a model eagerly.')
      self.target_tensors = target_tensors
  
      # Set DistributionStrategy specific parameters.
      self._distributed_model = None
      if self._distribution_strategy is not None:
        distributed_training_utils.configure_and_create_session(
            self._distribution_strategy)
      # Initialize model metric attributes.
      self._init_metric_attributes()
      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(training_utils.get_loss_function(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 = [training_utils.get_loss_function(l) for l in loss]
      else:
        loss_function = training_utils.get_loss_function(loss)
        loss_functions = [loss_function for _ in range(len(self.outputs))]
      self.loss_functions = 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(loss_functions)):
        if loss_functions[i] is None:
          skip_target_indices.append(i)
          skip_target_weighing_indices.append(i)
  
      # Prepare output masks.
      if not self.run_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
  
      # Initialization for Eager mode execution.
      if self.run_eagerly:
        # Prepare sample weights.
        self._set_sample_weight_attributes(sample_weight_mode,
                                           skip_target_weighing_indices)
        # Save all metric attributes per output of the model.
        self._cache_output_metric_attributes(metrics, weighted_metrics)
  
        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._compile_metrics_names.append(self.output_names[i] + '_loss')
  
        # Set metric attributes on model.
        self._set_metric_attributes(
            self.outputs,
            skip_target_indices=skip_target_indices,
        )
  
        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
  
      with K.get_graph().as_default():
        # 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 %s outputs, but you passed target_tensors=%s' %
                  (len(self.outputs), 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
          elif tensor_util.is_tensor(target_tensors):
            target_tensors = [target_tensors]
          else:
            raise TypeError('Expected `target_tensors` to be a list or tuple or '
                            'dict or a single tensor, 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_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get(
                    self.loss_functions[i],
                    K.dtype(self.outputs[i]))
  
                target = K.placeholder(
                    ndim=len(shape),
                    name=name + '_target',
                    sparse=K.is_sparse(self.outputs[i]),
                    dtype=target_dtype)
              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)
        # Save all metric attributes per output of the model.
        self._cache_output_metric_attributes(metrics, weighted_metrics)
  
        # 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]
            loss_fn = loss_functions[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'):
              if isinstance(loss_fn, losses.Loss):
                if mask is not None:
                  mask = math_ops.cast(mask, y_pred.dtype)
                  # Update weights with mask.
                  if sample_weight is None:
                    sample_weight = mask
                  else:
                    # Update dimensions of weights to match with mask if possible.
                    mask, _, sample_weight = squeeze_or_expand_dimensions(
                        mask, None, sample_weight)
                    sample_weight *= mask
                output_loss = loss_fn(y_true, y_pred, sample_weight=sample_weight)
              else:
                weighted_loss = training_utils.weighted_masked_objective(loss_fn)
                output_loss = weighted_loss(y_true, y_pred, sample_weight, mask)
  
            if len(self.outputs) > 1:
              # Keep track of the un-aggregated loss result tensor.
              self._compile_metrics_tensors[self.output_names[i] +
                                            '_loss'] = output_loss
  
              # Keep track of stateful result tensor and function for the loss.
              loss_name = loss_fn.name if isinstance(
                  loss_fn, losses.Loss) else loss_fn.__name__
              mean_wrapped_loss = metrics_module.MeanMetricWrapper(
                  loss_fn, name=loss_name)
              result_tensor = self._call_metric_fn(mean_wrapped_loss, y_true,
                                                   y_pred, sample_weight, mask)
              self._compile_stateful_metrics_tensors[self.output_names[i] +
                                                     '_loss'] = result_tensor
              self._compile_stateful_metric_functions.append(mean_wrapped_loss)
  
              self._compile_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
  
        # Set metric attributes on model.
        self._set_metric_attributes(
            self.outputs,
            skip_target_indices=skip_target_indices,
        )
        # 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._fit_function = None
        self._eval_function = None
        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
  
    @property
    def metrics(self):
      """Returns the model's metrics added using `compile`, `add_metric` APIs."""
      metrics = []
      if self._is_compiled:
        metrics += self._compile_stateful_metric_functions
      return metrics + super(Model, self).metrics
  
    @property
    def metrics_names(self):
      """Returns the model's display labels for all outputs."""
      metrics_names = []
      if self._is_compiled:
        metrics_names += self._compile_metrics_names  # Includes names of losses.
  
      # Add metric names from layers.
      for layer in self.layers:
        metrics_names += [m.name for m in layer._metrics]  # pylint: disable=protected-access
      metrics_names += [m.name for m in self._metrics]
      return metrics_names
  
    @property
    def run_eagerly(self):
      """Settable attribute indicating whether the model should run eagerly.
  
      Running eagerly means that your model will be run step by step,
      like Python code. Your model might run slower, but it should become easier
      for you to debug it by stepping into individual layer calls.
  
      By default, we will attempt to compile your model to a static graph to
      deliver the best execution performance.
  
      Returns:
        Boolean, whether the model should run eagerly.
      """
      if self._run_eagerly is True and not context.executing_eagerly():
        raise ValueError('You can only set `run_eagerly=True` if eager execution '
                         'is enabled.')
      if not self.dynamic:
        if self._run_eagerly is None:
          return False
        else:
          return self._run_eagerly
      else:
        if not context.executing_eagerly():
          raise ValueError('Your model contains layers that can only be '
                           'successfully run in eager execution (layers '
                           'constructed with `dynamic=True`). '
                           'You must enable eager execution with '
                           '`tf.enable_eager_execution()`.')
        if self._run_eagerly is False:
          # TODO(fchollet): consider using py_func to enable this.
          raise ValueError('Your model contains layers that can only be '
                           'successfully run in eager execution (layers '
                           'constructed with `dynamic=True`). '
                           'You cannot set `run_eagerly=False`.')
        return context.executing_eagerly()
  
    @run_eagerly.setter
    def run_eagerly(self, value):
      self._run_eagerly = value
  
    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,
            max_queue_size=10,
            workers=1,
            use_multiprocessing=False,
            **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. Should return a tuple
              of either `(inputs, targets)` or
              `(inputs, targets, sample_weights)`.
            - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
              or `(inputs, targets, sample weights)`.
          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, dataset
            iterator, generator, or `keras.utils.Sequence` instance, `y` should
            not be specified (since targets will be obtained from `x`).
          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, dataset, dataset iterators,
              generators, or `keras.utils.Sequence` instances (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 `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, dataset iterator, generator or
             `keras.utils.Sequence` instance.
          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
              For the first two cases, `batch_size` must be provided.
              For the last case, `validation_steps` must be provided.
          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, dataset iterator, generator, or
             `keras.utils.Sequence` instance, instead provide the sample_weights
              as the third element of `x`.
          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 `validation_data` is provided and
              is a dataset or dataset iterator. Total number of steps (batches of
              samples) to draw before stopping when performing validation
              at the end of every epoch.
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
              input only. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
              only. 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. Used for generator or
              `keras.utils.Sequence` input only. 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.
          **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.
      """
      # 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))
  
      # Case 1: distribution strategy.
      if self._distribution_strategy:
        return training_distributed.fit_distributed(
            self,
            x=x,
            y=y,
            batch_size=batch_size,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            validation_split=validation_split,
            validation_data=validation_data,
            shuffle=shuffle,
            class_weight=class_weight,
            sample_weight=sample_weight,
            initial_epoch=initial_epoch,
            steps_per_epoch=steps_per_epoch,
            validation_steps=validation_steps)
  
      batch_size = self._validate_or_infer_batch_size(
          batch_size, steps_per_epoch, x)
  
      # Case 2: generator-like. Input is Python generator, or Sequence object,
      # or a non-distributed Dataset or iterator in eager execution.
      if data_utils.is_generator_or_sequence(x):
        training_utils.check_generator_arguments(
            y, sample_weight, validation_split=validation_split)
        return self.fit_generator(
            x,
            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)
      if training_utils.is_eager_dataset_or_iterator(x):
        # Make sure that y, sample_weights, validation_split are not passed.
        training_utils.validate_dataset_input(x, y, sample_weight,
                                              validation_split)
        return self.fit_generator(
            x,
            steps_per_epoch=steps_per_epoch,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            validation_data=validation_data,
            validation_steps=validation_steps,
            class_weight=class_weight,
            workers=0,
            shuffle=shuffle,
            initial_epoch=initial_epoch)
  
      # Case 3: Symbolic tensors or Numpy array-like.
      # This includes Datasets and iterators in graph mode (since they
      # generate symbolic tensors).
      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,
          shuffle=shuffle)
  
      # Prepare validation data.
      if validation_data:
        val_x, val_y, val_sample_weights = self._unpack_validation_data(
            validation_data)
        val_x, val_y, val_sample_weights = self._standardize_user_data(
            val_x,
            val_y,
            sample_weight=val_sample_weights,
            batch_size=batch_size,
            steps=validation_steps,
            steps_name='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 self.run_eagerly:
        return training_generator.fit_generator(
            self, (x, y, sample_weights),
            steps_per_epoch=steps_per_epoch,
            batch_size=batch_size,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            validation_data=validation_data,
            validation_steps=validation_steps,
            workers=0,
            shuffle=shuffle,
            initial_epoch=initial_epoch)
      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,
                 callbacks=None,
                 max_queue_size=10,
                 workers=1,
                 use_multiprocessing=False):
      """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.
            - A generator or `keras.utils.Sequence` instance.
          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, dataset iterator, generator or
            `keras.utils.Sequence` instance, `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, dataset, dataset iterators,
              generators, or `keras.utils.Sequence` instances (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, instead pass
              sample weights as the third element of `x`.
          steps: Integer or `None`.
              Total number of steps (batches of samples)
              before declaring the evaluation round finished.
              Ignored with the default value of `None`.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during evaluation.
              See [callbacks](/api_docs/python/tf/keras/callbacks).
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
              input only. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
              only. 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. Used for generator or
              `keras.utils.Sequence` input only. 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.
  
      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.
      """
      # Case 1: distribution strategy.
      if self._distribution_strategy:
        return training_distributed.evaluate_distributed(
            self,
            x=x,
            y=y,
            batch_size=batch_size,
            verbose=verbose,
            sample_weight=sample_weight,
            steps=steps,
            callbacks=callbacks)
  
      batch_size = self._validate_or_infer_batch_size(batch_size, steps, x)
  
      # Case 2: generator-like. Input is Python generator, or Sequence object,
      # or a non-distributed Dataset or iterator in eager execution.
      if data_utils.is_generator_or_sequence(x):
        training_utils.check_generator_arguments(y, sample_weight)
        # TODO(fchollet): why aren't callbacks supported here?
        return self.evaluate_generator(
            x,
            steps=steps,
            verbose=verbose,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing)
      if training_utils.is_eager_dataset_or_iterator(x):
        # Make sure that y, sample_weights are not passed.
        training_utils.validate_dataset_input(x, y, sample_weight)
        return training_generator.evaluate_generator(
            self, x,
            steps=steps,
            batch_size=batch_size,
            verbose=verbose,
            workers=0,
            callbacks=callbacks)
  
      # Case 3: Symbolic tensors or Numpy array-like.
      # This includes Datasets and iterators in graph mode (since they
      # generate symbolic tensors).
      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 self.run_eagerly:
        return training_generator.evaluate_generator(
            self, (x, y, sample_weights),
            steps=steps,
            batch_size=batch_size,
            verbose=verbose,
            workers=0,
            callbacks=callbacks)
      else:
        return training_arrays.test_loop(
            self,
            inputs=x,
            targets=y,
            sample_weights=sample_weights,
            batch_size=batch_size,
            verbose=verbose,
            steps=steps,
            callbacks=callbacks)
  
    def predict(self,
                x,
                batch_size=None,
                verbose=0,
                steps=None,
                callbacks=None,
                max_queue_size=10,
                workers=1,
                use_multiprocessing=False):
      """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.
            - A generator or `keras.utils.Sequence` instance.
          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, dataset iterators,
              generators, or `keras.utils.Sequence` instances (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`.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during prediction.
              See [callbacks](/api_docs/python/tf/keras/callbacks).
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
              input only. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
              only. 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. Used for generator or
              `keras.utils.Sequence` input only. 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.
  
  
      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.
      """
      # Case 1: distribution strategy.
      if self._distribution_strategy:
        return training_distributed.predict_distributed(self,
                                                        x=x,
                                                        batch_size=batch_size,
                                                        verbose=verbose,
                                                        steps=steps,
                                                        callbacks=callbacks)
  
      batch_size = self._validate_or_infer_batch_size(batch_size, steps, x)
  
      # Case 2: generator-like. Input is Python generator, or Sequence object,
      # or a non-distributed Dataset or iterator in eager execution.
      if data_utils.is_generator_or_sequence(x):
        # TODO(fchollet): why aren't callbacks supported here?
        return self.predict_generator(
            x,
            steps=steps,
            verbose=verbose,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing)
      if training_utils.is_eager_dataset_or_iterator(x):
        return training_generator.predict_generator(
            self,
            x,
            steps=steps,
            batch_size=batch_size,
            verbose=verbose,
            workers=0,
            callbacks=callbacks)
  
      # Case 3: Symbolic tensors or Numpy array-like.
      # This includes Datasets and iterators in graph mode (since they
      # generate symbolic tensors).
      x, _, _ = self._standardize_user_data(
          x, check_steps=True, steps_name='steps', steps=steps)
  
      if self.run_eagerly:
        return training_generator.predict_generator(
            self,
            x,
            steps=steps,
            batch_size=batch_size,
            verbose=verbose,
            workers=0,
            callbacks=callbacks)
      else:
        return training_arrays.predict_loop(
            self,
            x,
            batch_size=batch_size,
            verbose=verbose,
            steps=steps,
            callbacks=callbacks)
  
    def reset_metrics(self):
      """Resets the state of metrics."""
      if hasattr(self, 'metrics'):
        for m in self.metrics:
          m.reset_states()
        if self._distribution_strategy:
          distributed_training_utils._reset_metrics(self)  # pylint: disable=protected-access
  
    def train_on_batch(self,
                       x,
                       y=None,
                       sample_weight=None,
                       class_weight=None,
                       reset_metrics=True):
      """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.
          reset_metrics: If `True`, the metrics returned will be only for this
            batch. If `False`, the metrics will be statefully accumulated across
            batches.
  
      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 self.run_eagerly:
        outputs = training_eager.train_on_batch(
            self, x, y, sample_weights=sample_weights)
      else:
        if not isinstance(K.symbolic_learning_phase(), int):
          ins = x + y + sample_weights + [True]
        else:
          ins = x + y + sample_weights
  
        if reset_metrics:
          self._make_train_function()
          outputs = self.train_function(ins)  # pylint: disable=not-callable
        else:
          self._make_fit_function()
          outputs = self._fit_function(ins)  # pylint: disable=not-callable
  
      if reset_metrics:
        self.reset_metrics()
  
      if len(outputs) == 1:
        return outputs[0]
      return outputs
  
    def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True):
      """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.
          reset_metrics: If `True`, the metrics returned will be only for this
            batch. If `False`, the metrics will be statefully accumulated across
            batches.
  
      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 self.run_eagerly:
        outputs = training_eager.test_on_batch(
            self, x, y, sample_weights=sample_weights)
      else:
        inputs = x + y + sample_weights
        if reset_metrics:
          self._make_test_function()
          outputs = self.test_function(inputs)  # pylint: disable=not-callable
        else:
          self._make_eval_function()
          outputs = self._eval_function(inputs)  # pylint: disable=not-callable
  
      if reset_metrics:
        self.reset_metrics()
  
      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 self.run_eagerly:
        if (isinstance(inputs, iterator_ops.EagerIterator) or
            (isinstance(inputs, dataset_ops.DatasetV2))):
          inputs = training_utils.cast_if_floating_dtype(inputs)
        elif isinstance(inputs, collections.Sequence):
          inputs = [
              ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs]
  
          # Unwrap lists with only one input, as we do when training on batch
          if len(inputs) == 1:
            inputs = inputs[0]
  
        return self(inputs)  # pylint: disable=not-callable
  
      self._make_predict_function()
      outputs = self.predict_function(inputs)
  
      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.')
      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,
                           callbacks=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 `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.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during evaluation.
              See [callbacks](/api_docs/python/tf/keras/callbacks).
          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.')
      return training_generator.evaluate_generator(
          self,
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose,
          callbacks=callbacks)
  
    def predict_generator(self,
                          generator,
                          steps=None,
                          callbacks=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 `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.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during prediction.
              See [callbacks](/api_docs/python/tf/keras/callbacks).
          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.')
      return training_generator.predict_generator(
          self,
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose,
          callbacks=callbacks)
  
    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
  
    def _make_callback_model(self, grouped_model):
      first_replicated_model = self._distribution_strategy.unwrap(
          grouped_model)[0]
      # We initialize the callback model with the first replicated model.
      self._replicated_model = DistributedCallbackModel(first_replicated_model)
      self._replicated_model.set_original_model(self)
  
    def _validate_or_infer_batch_size(self, batch_size, steps, x):
      """Validates that the `batch_size` provided is consistent with InputLayer.
  
      It's possible that the user specified a static batch size in their
      InputLayer. If so, this method checks the provided `batch_size` and `x`
      arguments are consistent with this static batch size. Also, if
      `batch_size` is `None`, this method will attempt to infer the batch size
      from the static batch size of the InputLayer. Lastly, ValueError will be
      raised if `x` is a tf.data.Dataset and `batch_size` is specified as we
      expect users to provide batched datasets.
  
      Arguments:
        batch_size: The batch_size provided as an argument to
          fit/evaluate/predict.
        steps: The steps provided as an argument to fit/evaluate/predict.
        x: The data passed as `x` to fit/evaluate/predict.
  
      Returns:
        The validated batch_size, auto-inferred from the first layer if not
        provided.
      """
      if batch_size is not None and isinstance(x, dataset_ops.DatasetV2):
        raise ValueError('The `batch_size` argument must not be specified when'
                         ' using dataset as an input.')
  
      layers = super(Model, self).layers  # Avoids the override in Sequential.
      if layers:
        first_layer = layers[0]
        static_batch_size = training_utils.get_static_batch_size(first_layer)
        if static_batch_size is not None:
  
          # Check `batch_size` argument is consistent with InputLayer.
          if batch_size is not None and batch_size != static_batch_size:
            raise ValueError('The `batch_size` argument value {} is incompatible '
                             'with the specified batch size of your Input Layer: '
                             '{}'.format(batch_size, static_batch_size))
  
          # Check Dataset/Iterator batch size is consistent with InputLayer.
          if isinstance(x, (dataset_ops.DatasetV2, iterator_ops.Iterator,
                            iterator_ops.EagerIterator)):
            ds_batch_size = tensor_shape.as_dimension(
                nest.flatten(x.output_shapes)[0][0]).value
            if ds_batch_size is not None and ds_batch_size != static_batch_size:
              raise ValueError('The batch output shape of your `Dataset` is {}, '
                               'which is incompatible with the specified batch '
                               'size of your Input Layer: {}'.format(
                                   ds_batch_size, static_batch_size))
  
          # Set inferred batch size from the InputLayer.
          if steps is None:
            batch_size = static_batch_size
  
      if batch_size is None and steps is None:
        # Backwards compatibility
        batch_size = 32
      return batch_size
  
    @property
    def _default_save_signature(self):
      return training_utils.trace_model_call(self)
  
    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 _cache_output_metric_attributes(self, metrics, weighted_metrics):
      """Caches metric name and function attributes for every model output."""
      output_shapes = []
      for output in self.outputs:
        if output is None or output.shape.rank is None:
          output_shapes.append(None)
        else:
          output_shapes.append(output.shape.as_list())
      self._per_output_metrics = training_utils.collect_per_output_metric_info(
          metrics, self.output_names, output_shapes, self.loss_functions)
      self._per_output_weighted_metrics = \
          training_utils.collect_per_output_metric_info(
              weighted_metrics, self.output_names, output_shapes,
              self.loss_functions, self.sample_weights)
  
    def _add_unique_metric_name(self, metric_name, output_index):
      """Makes the metric name unique and adds it to the model's metric name list.
  
        If there are multiple outputs for which the metrics are calculated, the
        metric names have to be made unique by appending an integer.
  
      Arguments:
        metric_name: Metric name that corresponds to the metric specified by the
            user. For example: 'acc'.
        output_index: The index of the model output for which the metric name is
          being added.
  
      Returns:
        string, name of the model's unique metric name
      """
      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._compile_metrics_names:
        metric_name = '%s_%d' % (base_metric_name, j)
        j += 1
  
      return metric_name
  
    @property
    def _all_metrics_tensors(self):
      """Returns the network's symbolic metric tensors."""
      metrics_tensors = {}
      if self._is_compiled:
        metrics_tensors.update(self._compile_metrics_tensors)
      metrics_tensors.update(super(Model, self)._all_metrics_tensors)
      return metrics_tensors
  
    @property
    def _all_stateful_metrics_tensors(self):
      """Returns the network's symbolic metric tensors."""
      metrics_tensors = {}
      if self._is_compiled:
        metrics_tensors.update(self._compile_stateful_metrics_tensors)
      metrics_tensors.update(super(Model, self)._all_metrics_tensors)
      return metrics_tensors
  
    def _init_metric_attributes(self):
      """Initialized model metric attributes."""
      # List of all metric names in the model.
      self._compile_metrics_names = ['loss']
      # List of stateful metric functions. Used for resetting metric state during
      # training/eval.
      # This includes loss functions when there are multiple outputs.
      self._compile_stateful_metric_functions = []
      # Dict of all aggregated metric result tensors. This includes aggregated
      # loss result tensors when there are multiple outputs.
      self._compile_stateful_metrics_tensors = {}
      # Dict of all metric result tensors (aggregated or not - based on the
      # values given in compile.). This includes aggregated loss result tensors
      # when there are multiple outputs.
      self._compile_metrics_tensors = {}
  
    def _set_per_output_metric_attributes(self, metrics_dict, output_index):
      """Sets the metric attributes on the model for the given output.
  
      Arguments:
        metrics_dict: A dict with metric names as keys and metric fns as values.
        output_index: The index of the model output for which the metric
          attributes are added.
  
      Returns:
        Metrics dict updated with unique metric names as keys.
      """
      updated_metrics_dict = collections.OrderedDict()
      for metric_name, (metric_fn, stateful_metric_fn) in metrics_dict.items():
        metric_name = self._add_unique_metric_name(metric_name, output_index)
        updated_metrics_dict[metric_name] = (metric_fn, stateful_metric_fn)
        # Keep track of metric name, function and stateful function.
        self._compile_metrics_names.append(metric_name)
        self._compile_stateful_metric_functions.append(stateful_metric_fn)
      return updated_metrics_dict
  
    def _set_metric_attributes(self, outputs, skip_target_indices=None):
      """Sets the metric attributes on the model for all the model outputs."""
      skip_target_indices = skip_target_indices or []
      updated_per_output_metrics = []
      updated_per_output_weighted_metrics = []
      for i in range(len(outputs)):
        if i in skip_target_indices:
          updated_per_output_metrics.append(self._per_output_metrics[i])
          updated_per_output_weighted_metrics.append(
              self._per_output_weighted_metrics[i])
          continue
        updated_per_output_metrics.append(
            self._set_per_output_metric_attributes(self._per_output_metrics[i],
                                                   i))
        updated_per_output_weighted_metrics.append(
            self._set_per_output_metric_attributes(
                self._per_output_weighted_metrics[i], i))
  
      self._per_output_metrics = updated_per_output_metrics
      self._per_output_weighted_metrics = updated_per_output_weighted_metrics
  
    def _call_metric_fn(self, fn, y_true, y_pred, weights, mask):
      """Helper function to call metric function with distribution strategy."""
      # TODO(b/120571621): We want to avoid metric reductions here since
      # since TPUStrategy does not implement replica local variables.
      # Remove this hack once we support TPUReplicaLocalVariables.
      is_tpu = distributed_training_utils.is_tpu_strategy(
          self._distribution_strategy)
      if ((not is_tpu) and self._distribution_strategy and
          distribution_strategy_context.in_cross_replica_context()):
        with self._distribution_strategy.scope():
          return self._distribution_strategy.extended.call_for_each_replica(
              training_utils.call_metric_function,
              (fn, y_true, y_pred, weights, mask))
      return training_utils.call_metric_function(
          fn, y_true, y_pred, weights=weights, mask=mask)
  
    def _handle_per_output_metrics(self,
                                   metrics_dict,
                                   y_true,
                                   y_pred,
                                   mask,
                                   weights=None,
                                   return_stateful_result=True):
      """Calls metric functions for a single output.
  
      Arguments:
        metrics_dict: A dict with metric names as keys and metric fns as values.
        y_true: Target output.
        y_pred: Predicted output.
        mask: Computed mask value for the current output.
        weights: Weights to be applied on the current output.
        return_stateful_result: Boolean, indicates whether the stateful
          (aggregated)/stateless metric result should be returned.
  
      Returns:
        A list of metric result tensors.
      """
      metric_results = []
      for metric_name, (metric_fn, stateful_fn) in metrics_dict.items():
        with K.name_scope(metric_name):
  
          def _call_stateful_fn(fn):
            """Create stateful metrics correctly."""
            return self._call_metric_fn(fn, y_true, y_pred, weights, mask)
  
          def _call_stateless_fn(fn):
            weighted_metric_fn = training_utils.weighted_masked_objective(fn)
            return weighted_metric_fn(y_true, y_pred, weights=weights, mask=mask)
  
          def _track_metric_tensors(name, stateless_result, stateful_result):
            self._compile_metrics_tensors[name] = stateless_result
            self._compile_stateful_metrics_tensors[name] = stateful_result
  
          if isinstance(metric_fn, metrics_module.Metric):
            # If the given metric fn is stateful, call the fn and return result.
            metric_result = _call_stateful_fn(metric_fn)
            metric_results.append(metric_result)
            if not self.run_eagerly:
              _track_metric_tensors(metric_name, metric_result, metric_result)
          elif self.run_eagerly:
            # In eager mode, if the given metric fn is not stateful, we invoke the
            # given fn or its stateful version based on the given flag.
            if return_stateful_result:
              metric_result = _call_stateful_fn(stateful_fn)
            else:
              metric_result = _call_stateless_fn(metric_fn)
            metric_results.append(metric_result)
          else:
            # In graph mode, we build the sub-graph for both the stateful and the
            # stateless fns.
            stateful_metric_result = _call_stateful_fn(stateful_fn)
            metric_result = _call_stateless_fn(metric_fn)
            _track_metric_tensors(metric_name, metric_result,
                                  stateful_metric_result)
  
      return metric_results
  
    def _handle_metrics(self,
                        outputs,
                        skip_target_indices=None,
                        targets=None,
                        sample_weights=None,
                        masks=None,
                        return_stateful_result=True):
      """Handles calling metric functions.
  
      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.
        return_stateful_result: Boolean, indicates whether the stateful
          (aggregated)/stateless metric result should be returned.
  
      Returns:
        A list of metric result tensors.
      """
      skip_target_indices = skip_target_indices or []
      metric_results = []
      with K.name_scope('metrics'):
        # Invoke all metrics added using `compile`.
        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_mask = masks[i] if masks else None
          metric_results.extend(
              self._handle_per_output_metrics(
                  self._per_output_metrics[i],
                  target,
                  output,
                  output_mask,
                  return_stateful_result=return_stateful_result))
          metric_results.extend(
              self._handle_per_output_metrics(
                  self._per_output_weighted_metrics[i],
                  target,
                  output,
                  output_mask,
                  weights=sample_weights[i],
                  return_stateful_result=return_stateful_result))
  
      # Add metric results from the `add_metric` metrics in eager mode.
      if context.executing_eagerly():
        for m in self.metrics:
          if m not in self._compile_stateful_metric_functions:
            metric_results.append(m.result())
      return metric_results
  
    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.log_first_n(
            logging.WARN, 'Discrepancy between trainable weights and collected'
            ' trainable weights, did you set `model.trainable`'
            ' without calling `model.compile` after ?', 1)
  
    def _make_train_function_helper(self, fn_name, outputs, metric_updates=None):
      if not hasattr(self, fn_name):
        raise RuntimeError('You must compile your model before using it.')
      self._check_trainable_weights_consistency()
      if getattr(self, fn_name) is None:
        inputs = (self._feed_inputs +
                  self._feed_targets +
                  self._feed_sample_weights)
        if not isinstance(K.symbolic_learning_phase(), int):
          inputs += [K.symbolic_learning_phase()]
  
        with K.get_graph().as_default():
          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)
        # Add stateful metrics updates.
        if metric_updates is not None:
          updates += metric_updates
  
        with K.name_scope('training'):
          # Gets loss and metrics. Updates weights at each call.
          fn = K.function(
              inputs,
              outputs,
              updates=updates,
              name='train_function',
              **self._function_kwargs)
          setattr(self, fn_name, fn)
  
    def _make_train_function(self):
      metrics_tensors = [
          self._all_metrics_tensors[m] for m in self.metrics_names[1:]
      ]
      self._make_train_function_helper('train_function',
                                       [self.total_loss] + metrics_tensors)
  
    def _make_fit_function(self):
      metrics_tensors = [
          self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:]
      ]
      self._make_train_function_helper(
          '_fit_function', [self.total_loss] + metrics_tensors)
  
    def _make_test_function_helper(self, fn_name, outputs, metric_updates=None):
      if not hasattr(self, fn_name):
        raise RuntimeError('You must compile your model before using it.')
      if getattr(self, fn_name) is None:
        inputs = (self._feed_inputs +
                  self._feed_targets +
                  self._feed_sample_weights)
  
        with K.name_scope('evaluation'):
          updates = self.state_updates
          # Add stateful metrics updates.
          if metric_updates is not None:
            updates += metric_updates
          # Return loss and metrics, no gradient updates.
          # Does update the network states.
          fn = K.function(
              inputs,
              outputs,
              updates=updates,
              name='test_function',
              **self._function_kwargs)
          setattr(self, fn_name, fn)
  
    def _make_test_function(self):
      metrics_tensors = [
          self._all_metrics_tensors[m] for m in self.metrics_names[1:]
      ]
      self._make_test_function_helper('test_function',
                                      [self.total_loss] + metrics_tensors)
  
    def _make_eval_function(self):
      metrics_tensors = [
          self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:]
      ]
      self._make_test_function_helper(
          '_eval_function', [self.total_loss] + metrics_tensors)
  
    def _make_predict_function(self):
      if not hasattr(self, 'predict_function'):
        self.predict_function = None
      if self.predict_function is None:
        inputs = self._feed_inputs
        # Gets network outputs. Does not update weights.
        # Does update the network states.
        kwargs = getattr(self, '_function_kwargs', {})
        with K.name_scope(ModeKeys.PREDICT):
          self.predict_function = K.function(
              inputs,
              self.outputs,
              updates=self.state_updates,
              name='predict_function',
              **kwargs)
  
    def _make_execution_function(self, mode):
      if mode == ModeKeys.TRAIN:
        self._make_fit_function()
        return self._fit_function
      if mode == ModeKeys.TEST:
        self._make_eval_function()
        return self._eval_function
      if mode == ModeKeys.PREDICT:
        self._make_predict_function()
        return self.predict_function
  
    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,
                                            shuffle=False):
      """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 numpy array or `tf.data` dataset.
        y: Target data. A numpy array or None if x is a `tf.data` dataset.
        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.
        shuffle: Boolean whether to shuffle the training data before each epoch.
  
      Returns:
        Iterator for reading the dataset `x`.
  
      Raises:
        ValueError: In case of invalid user-provided data.
        RuntimeError: If the model was never compiled.
      """
      if class_weight:
        raise NotImplementedError('`class_weight` is currently not supported '
                                  'when using DistributionStrategy.')
  
      if (sample_weight is not None and sample_weight.all() and
          distributed_training_utils.is_tpu_strategy(
              self._distribution_strategy)):
        raise NotImplementedError('`sample_weight` is currently not supported '
                                  'when using TPUStrategy.')
  
      # Validates `steps` argument right at the beginning since we use it to
      # construct the dataset object.
      # TODO(anjalisridhar): Remove this check once we refactor the
      # _standardize_user_data code path. This check is already present elsewhere
      # in the codebase.
      if check_steps and isinstance(x, dataset_ops.DatasetV2) and steps is None:
        raise ValueError('When using Datasets as input, '
                         'you should specify the `{steps_name}` argument.'
                         .format(steps_name=steps_name))
  
      first_x_value = nest.flatten(x)[0]
      if isinstance(first_x_value, np.ndarray):
        # We need to use the drop_remainder argument to allow for a static
        # input shape which is required for TPUs.
        drop_remainder = self._distribution_strategy.require_static_shapes
        if y is not None:
          var_x = distributed_training_utils.get_var_for_numpy(
              self._distribution_strategy, x)
          var_y = distributed_training_utils.get_var_for_numpy(
              self._distribution_strategy, y)
          if sample_weight is not None:
            var_sample_weights = distributed_training_utils.get_var_for_numpy(
                self._distribution_strategy, sample_weight)
  
            x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y,
                                                        var_sample_weights))
          else:
            x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y))
  
          x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y))
          if shuffle:
            # 1024 is a good buffer size since it is much larger than the average
            # batch size provided by the user and provides sufficient randomness.
            # One thing to keep in mind is the memory usage based on the size of
            # each sample.
            x = x.shuffle(1024)
          x = x.repeat()
          x = x.batch(batch_size, drop_remainder=drop_remainder)
          y = None
          sample_weight = None
        else:
          # This case is for the predict call where the dataset only contains
          # inputs and no targets, i.e. it does not return a tuple
          var_x = distributed_training_utils.get_var_for_numpy(
              self._distribution_strategy, x)
          x = dataset_ops.Dataset.from_tensor_slices(var_x)
          x = x.batch(batch_size, drop_remainder=drop_remainder)
  
      assert isinstance(x, dataset_ops.DatasetV2)
  
      with self._distribution_strategy.scope():
        iterator = self._distribution_strategy.make_dataset_iterator(x)
        init_op = iterator.initialize()
        if not context.executing_eagerly():
          K.get_session().run(init_op)
  
      training_utils.validate_dataset_input(x, y, sample_weight,
                                            validation_split)
      return iterator
  
    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,
                               shuffle=False):
      """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`. If both `sample_weight` and `class_weight` are
          provided, the weights are multiplied.
        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.
        shuffle: Boolean whether to shuffle the training data before each epoch.
  
      Returns:
        A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a dict
        or not), 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 isinstance(x, dataset_ops.DatasetV2):
        if context.executing_eagerly():
          x = iter(x)
        else:
          if x in self._dataset_iterator_cache:
            x = self._dataset_iterator_cache[x]
          else:
            iterator = dataset_ops.make_initializable_iterator(x)
            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_dataset_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:
        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:
        try:
          next_element = self._get_iterator_get_next_tensors(x)
        except errors.OutOfRangeError:
          raise RuntimeError('Your dataset iterator ran out of data; '
                             'Make sure that your dataset can generate '
                             'required number of samples.')
  
        if isinstance(next_element, (list, tuple)):
          if len(next_element) not in [2, 3]:
            raise ValueError(
                'Please provide model inputs as a list or tuple of 2  or 3'
                'elements: (input, target) or (input, target, sample_weights)'
                'Received %s' % next_element)
          if len(next_element) == 2:
            x, y = next_element
          else:
            x, y, sample_weight = next_element
        else:
          x = next_element
  
      # First, we build/compile the model on the fly if necessary.
      all_inputs = []
      is_build_called = False
      is_compile_called = False
      # Whether this is a subclassed model that expects dictionary inputs
      # rather than list inputs (e.g. FeatureColumn-based models).
      dict_inputs = 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):
          dict_inputs = True
          keys = sorted(x.keys())
          all_inputs = [x[k] for k in keys]
        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 or generated from a dataset, then in symbolic-mode
        # placeholders will be created to match the value shapes.
        is_build_called = True
        if is_x_iterator:
          cast_inputs = nest.map_structure(lambda v: v.shape, x)
        elif training_utils.has_tensors(x):
          cast_inputs = training_utils.cast_if_floating_dtype(x)
        else:
          cast_inputs = x
        self._set_inputs(cast_inputs)
      else:
        dict_inputs = isinstance(self.inputs, dict)
      if dict_inputs and context.executing_eagerly():
        # No support for graph functions when the model expects dictionary inputs
        # (i.e. FeatureColumn-based models).
        self.run_eagerly = True
  
      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 training_utils.has_tensors(y):
            y = training_utils.cast_if_floating_dtype(y)
          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 self.run_eagerly or is_x_iterator:
            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 _is_symbolic_tensor(v)]
          is_compile_called = True
          self.compile(
              optimizer=self.optimizer,
              loss=self.loss,
              metrics=self._compile_metrics,
              weighted_metrics=self._compile_weighted_metrics,
              loss_weights=self.loss_weights,
              target_tensors=target_tensors,
              run_eagerly=self.run_eagerly)
  
      # 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 self.run_eagerly and is_build_called and is_compile_called and
          not is_x_iterator and any(_is_symbolic_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 self.run_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,
            # Don't enforce target shapes to match output shapes.
            # Precise checks will be run in `check_loss_and_target_compatibility`.
            shapes=None,
            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 self.run_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')
  
      # If dictionary inputs were provided, we return a dictionary as well.
      if dict_inputs:
        x = dict(zip(feed_input_names, x))
      return x, y, sample_weights
  
    def _unpack_validation_data(self, validation_data):
      if (isinstance(validation_data, (iterator_ops.Iterator,
                                       iterator_ops.EagerIterator,
                                       dataset_ops.DatasetV2))):
        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)
      return val_x, val_y, val_sample_weight
  
    @checkpointable.no_automatic_dependency_tracking
    def _set_inputs(self, inputs, outputs=None, 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, data tensors, or TensorShapes.
          - 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 or TensorShapes: we create placeholders matching the
            TensorShapes or shapes 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.
        outputs: None, a data tensor, or a list of 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 dict inputs are passed to a Sequential Model where the
          first layer isn't FeatureLayer.
      """
      if self.inputs:
        raise ValueError('Model inputs are already set.')
  
      if self.__class__.__name__ == 'Sequential' and not self.built:
        if tensor_util.is_tensor(inputs):
          input_shape = (None,) + tuple(inputs.shape.as_list()[1:])
        elif isinstance(inputs, tensor_shape.TensorShape):
          input_shape = (None,) + tuple(inputs.as_list()[1:])
        elif isinstance(inputs, dict):
          # We assert that the first layer is a FeatureLayer.
          if not training_utils.is_feature_layer(self.layers[0]):
            raise ValueError('Passing a dictionary input to a Sequential Model '
                             'which doesn\'t have FeatureLayer as the first layer'
                             ' is an error.')
          input_shape = (None,)
        else:
          input_shape = (None,) + tuple(inputs.shape[1:])
        self._build_input_shape = input_shape
  
      # On-the-fly setting of symbolic model inputs (either by using the tensor
      # provided, or by creating a placeholder if Numpy data was provided).
      model_inputs = training_utils.ModelInputs(inputs)
      inputs = model_inputs.get_symbolic_inputs()
      self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True)
      self.input_names = model_inputs.get_input_names()
  
      self._feed_inputs = []
      self._feed_input_names = []
      self._feed_input_shapes = []
  
      for k, v in model_inputs.as_dict():
        if K.is_placeholder(v):
          self._feed_input_names.append(k)
          self._feed_inputs.append(v)
          self._feed_input_shapes.append(K.int_shape(v))
  
      # TODO(fchollet): consider calling `_maybe_build` before calling the model.
      if outputs is None:
        if not self._dynamic:
          # The network may include dynamic layers but its `call`
          # itself isn't dynamic.
          # Obtain symbolic outputs by calling the model.
          with K.get_graph().as_default():
            if self._expects_training_arg:
              outputs = self.call(inputs, training=training)
            else:
              outputs = self.call(inputs)
        else:
          # Case: network's `call` is dynamic.
          try:
            outputs = self._symbolic_call(inputs)
          except NotImplementedError:
            # Static shape inference was not implemented for this dynamic net.
            # Do not specify symbolic outputs.
            outputs = None
  
      outputs = nest.flatten(outputs)
      self.outputs = outputs
      self.output_names = training_utils.generic_output_names(outputs)
      self.built = True
  
  
  class DistributedCallbackModel(Model):
    """Model that is used for callbacks with DistributionStrategy."""
  
    def __init__(self, model):
      super(DistributedCallbackModel, self).__init__()
      self.optimizer = model.optimizer
  
    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.')
  
  
  def _is_symbolic_tensor(x):
    return tensor_util.is_tensor(x) and not isinstance(x, ops.EagerTensor)
  