def test_on_batch(model, inputs, targets, sample_weights=None):
  """Calculates the loss for one input batch.

  Arguments:
      model: Model whose loss has to be calculated.
      inputs: Input batch data.
      targets: Target batch data.
      sample_weights: Sample weight batch data.

  Returns:
      total loss, loss and metrics associated with each output.
  """
  if len(inputs) and tensor_util.is_tensor(inputs[0]):
    inputs = training_utils.cast_if_floating_dtype(inputs)
    targets = training_utils.cast_if_floating_dtype(targets)
  else:
    inputs = [
        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs
    ]
    targets = [
        ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets
    ]
  if sample_weights:
    sample_weights = [
        ops.convert_to_tensor(val, dtype=backend.floatx())
        if val is not None else None for val in sample_weights
    ]
  outs, loss, loss_metrics = _model_loss(
      model, inputs, targets, sample_weights=sample_weights, training=False)
  if not isinstance(outs, list):
    outs = [outs]
  metrics_results = _eager_metrics_fn(model, outs, targets)
  if not isinstance(loss, list):
    loss = [loss]
  return loss + loss_metrics + metrics_results

def train_on_batch(model,
                   inputs,
                   targets,
                   sample_weights=None,
                   output_loss_metrics=None):
  """Calculates the loss and gradient updates for one input batch.

  Arguments:
      model: Model whose loss has to be calculated.
      inputs: Input batch data.
      targets: Target batch data.
      sample_weights: Sample weight batch data.
      output_loss_metrics: List of metrics that are used to aggregated output
        loss values.

  Returns:
      total loss and the loss associated with each output.
  """
  if isinstance(inputs, collections.Sequence):
    if len(inputs) and tensor_util.is_tensor(inputs[0]):
      inputs = training_utils.cast_if_floating_to_model_input_dtypes(inputs,
                                                                     model)
      if targets:
        targets = training_utils.cast_if_floating_dtype(targets)
    else:
      inputs = training_utils.cast_if_floating_to_model_input_dtypes(
          [ops.convert_to_tensor(val) for val in inputs], model)
      if targets:
        targets = training_utils.cast_if_floating_dtype(
            [ops.convert_to_tensor(val) for val in targets])
  if sample_weights:
    sample_weights = [
        training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
        if val is not None else None for val in sample_weights
    ]

  outs, total_loss, output_losses, masks = (
      _process_single_batch(
          model,
          inputs,
          targets,
          sample_weights=sample_weights,
          training=True,
          output_loss_metrics=output_loss_metrics))
  if not isinstance(outs, list):
    outs = [outs]
  metrics_results = _eager_metrics_fn(
      model, outs, targets, sample_weights=sample_weights, masks=masks)
  total_loss = nest.flatten(total_loss)
  results = total_loss + output_losses + metrics_results

  return [_non_none_constant_value(v) for v in results]

def train_on_batch(model, inputs, targets, sample_weights=None):
  """Calculates the loss and gradient updates for one input batch.

  Arguments:
      model: Model whose loss has to be calculated.
      inputs: Input batch data.
      targets: Target batch data.
      sample_weights: Sample weight batch data.

  Returns:
      total loss and the loss associated with each output.
  """
  if isinstance(inputs, collections.Sequence):
    if len(inputs) and tensor_util.is_tensor(inputs[0]):
      inputs = training_utils.cast_if_floating_dtype(inputs)
      targets = training_utils.cast_if_floating_dtype(targets)
    else:
      inputs = training_utils.cast_if_floating_dtype([
          ops.convert_to_tensor(val) for val in inputs
      ])
      targets = training_utils.cast_if_floating_dtype([
          ops.convert_to_tensor(val) for val in targets
      ])
  if sample_weights:
    sample_weights = [
        training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
        if val is not None else None for val in sample_weights
    ]

  outs, loss, loss_metrics, _, masks = _process_single_batch(
      model, inputs, targets, sample_weights=sample_weights, training=True)
  if not isinstance(outs, list):
    outs = [outs]
  metrics_results = _eager_metrics_fn(
      model,
      outs,
      targets,
      sample_weights=sample_weights,
      masks=masks,
      return_stateful_result=True)
  loss = nest.flatten(loss)

  return [
      tensor_util.constant_value(v)
      for v in loss + loss_metrics + metrics_results
  ]

def iterator_predict_loop(model, inputs, steps, verbose=0):
  """Predict function for eager execution when input is dataset iterator.

  Arguments:
      model: Instance of `Model`.
      inputs: Input dataset iterator.
      steps: Total number of steps (batches of samples) before declaring
          `_predict_loop` finished.
      verbose: Verbosity mode.

  Returns:
      Array of predictions (if the model has a single output)
      or list of arrays of predictions (if the model has multiple outputs).

  Raises:
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  assert isinstance(inputs, iterator_ops.EagerIterator)
  if not isinstance(inputs.output_shapes,
                    (list, tuple)) or len(inputs.output_shapes) > 3:
    raise ValueError(
        'Please provide data as a list or tuple of 1, 2, or 3 elements '
        ' - `(input)`, or `(input, target)`, or `(input, target,'
        'sample_weights)`. Received %s. We do not use the `target` or'
        '`sample_weights` value here.' % inputs.output_shapes)
  outs = []
  if verbose == 1:
    progbar = generic_utils.Progbar(target=steps)
  for step_index in range(steps):
    # Get data from the iterator.
    try:
      next_element = inputs.get_next()
    except errors.OutOfRangeError:
      logging.warning(
          'Your dataset iterator ran out of data; interrupting prediction. '
          'Make sure that your dataset can generate at least `steps` batches '
          '(in this case, %d batches). You may need to use the repeat() '
          'function when building your dataset.', steps)
      break

    # expects a tuple, where first element of tuple represents inputs
    x = next_element[0]

    # Validate and standardize data.
    x, _, _ = model._standardize_user_data(x)
    x = training_utils.cast_if_floating_dtype(x)

    if isinstance(x, list) and len(x) == 1:
      x = x[0]

    if model._expects_training_arg:
      batch_outs = model.call(x, training=False)
    else:
      batch_outs = model.call(x)
    if not isinstance(batch_outs, list):
      batch_outs = [batch_outs]

    # We collect the results from every step and then concatenate them once
    # in the end. This is an expensive process. We are doing this because we
    # do not know the number of samples beforehand.
    if step_index == 0:
      for _ in batch_outs:
        outs.append([])
    for i, batch_out in enumerate(batch_outs):
      outs[i].append(backend.get_value(batch_out))

    if verbose == 1:
      progbar.update(step_index + 1)
  for i, out in enumerate(outs):
    outs[i] = np.concatenate(tuple(out), axis=0)
  if len(outs) == 1:
    return outs[0]
  return outs

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

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

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

  Raises:
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  assert isinstance(inputs, iterator_ops.EagerIterator)
  # make sure either x,y or x,y,sample_weights is provided
  if (not isinstance(inputs.output_shapes, (list, tuple)) or
      len(inputs.output_shapes) < 2 or len(inputs.output_shapes) > 3):
    raise ValueError('Please provide either inputs and targets'
                     'or inputs, targets, and sample_weights')
  outs = []
  num_samples = 0
  if verbose == 1:
    progbar = generic_utils.Progbar(target=steps)
  for step_index in range(steps):
    # Get data from the iterator.
    try:
      next_element = inputs.get_next()
    except errors.OutOfRangeError:
      logging.warning(
          'Your dataset iterator ran out of data interrupting testing. '
          'Make sure that your dataset can generate at least `steps` batches '
          '(in this case, %d batches). You may need to use the repeat() '
          'function when building your dataset.', steps)
      break

    if len(inputs.output_shapes) == 2:
      x, y = next_element
      sample_weights = None
    else:
      x, y, sample_weights = next_element

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

    if step_index == 0:
      # Get stateful metrics indices. We do not do this before the `steps` loop
      # because model will be compiled only in the first iteration of this loop
      # in the deferred build scenario.
      if hasattr(model, 'metrics'):
        for m in model.stateful_metric_functions:
          m.reset_states()
        stateful_metric_indices = [
            i for i, name in enumerate(model.metrics_names)
            if str(name) in model.stateful_metric_names
        ]
      else:
        stateful_metric_indices = []

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

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

    # Accumulate results in output array.
    if not isinstance(batch_outs, list):
      batch_outs = [batch_outs]
    if step_index == 0:
      for _ in enumerate(batch_outs):
        outs.append(0.)
    for i, batch_out in enumerate(batch_outs):
      if i in stateful_metric_indices:
#.........这里部分代码省略.........

def iterator_fit_loop(model,
                      inputs,
                      class_weight,
                      steps_per_epoch,
                      epoch_logs,
                      val_inputs=None,
                      val_targets=None,
                      val_sample_weights=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      validation_steps=None,
                      do_validation=False,
                      batch_size=None):
  """Fit function for eager execution when input is given as dataset iterator.

  Updates the given epoch logs.

  Arguments:
      model: Instance of the `Model`.
      inputs: Input dataset iterator.
      class_weight: Optional class-weight array to weight the importance of
          samples in `inputs` based on the class they belong to, as conveyed by
          the targets from the `inputs` iterator.
      steps_per_epoch: Total number of steps (batches of samples)
          before declaring one epoch finished and starting the
          next epoch.
      epoch_logs: Dictionary of logs from every epoch.
      val_inputs: Input data for validation.
      val_targets: Target data for validation.
      val_sample_weights: Sample weight data for validation.
      epochs: Number of times to iterate over the data
      verbose: Verbosity mode, 0, 1 or 2
      callbacks: CallbackList instance. Controls callbacks during training.
      validation_steps: Number of steps to run validation for (only if doing
        validation from data tensors). Ignored with default value of `None`.
      do_validation: Boolean value indicating whether we should do validation.
      batch_size: int, val_inputs and val_targets will be evaled batch by
        batch with size batch_size if they are array.

  Raises:
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  assert isinstance(inputs, iterator_ops.EagerIterator)

  # make sure either x,y or x,y,sample_weights is provided
  if (not isinstance(inputs.output_shapes, (list, tuple)) or
      len(inputs.output_shapes) not in (2, 3)):
    raise ValueError('Please provide either inputs and targets '
                     'or inputs, targets, and sample_weights')

  for step_index in range(steps_per_epoch):
    batch_logs = {'batch': step_index, 'size': 1}
    callbacks.on_batch_begin(step_index, batch_logs)

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

    if len(inputs.output_shapes) == 2:
      x, y = next_element
      sample_weights = None
    else:
      x, y, sample_weights = next_element

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

    # Set stateful_metrics in callbacks. We do not do this before the
    # `steps_per_epoch` loop because model will be compiled only in the first
    # iteration of this loop in the deferred build scenario.
    if step_index == 0:
      for cbk in callbacks:
        if (isinstance(cbk, cbks.BaseLogger) or
            isinstance(cbk, cbks.ProgbarLogger)):
          cbk.stateful_metrics = model.stateful_metric_names

    if step_index == 0 and not callbacks.params['metrics']:
      callback_metrics = copy.copy(model.metrics_names)
      if do_validation:
        callback_metrics += ['val_' + n for n in model.metrics_names]
      callbacks.set_params({
#.........这里部分代码省略.........

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

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

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

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

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

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

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

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

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

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

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

def iterator_fit_loop(model,
                      inputs,
                      class_weight,
                      steps_per_epoch,
                      callback_model,
                      out_labels,
                      epoch_logs,
                      val_inputs=None,
                      val_targets=None,
                      val_sample_weights=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      callback_metrics=None,
                      validation_steps=None,
                      do_validation=False):
  """Fit function for eager execution when input is given as dataset iterator.

  Updates the given epoch logs.

  Arguments:
      model: Instance of the `Model`.
      inputs: Input dataset iterator.
      class_weight: Optional class-weight array to weight the importance of
          samples in `inputs` based on the class they belong to, as conveyed by
          the targets from the `inputs` iterator.
      steps_per_epoch: Total number of steps (batches of samples)
          before declaring one epoch finished and starting the
          next epoch.
      callback_model: Instance of `Model` to callback.
      out_labels: Output labels generated from model metric names.
      epoch_logs: Dictionary of logs from every epoch.
      val_inputs: Input data for validation.
      val_targets: Target data for validation.
      val_sample_weights: Sample weight data for validation.
      epochs: Number of times to iterate over the data
      verbose: Verbosity mode, 0, 1 or 2
      callbacks: List of callbacks to be called during training
      callback_metrics: List of strings, the display names of the metrics
          passed to the callbacks. They should be the
          concatenation of list the display names of the outputs of
           `f` and the list of display names of the outputs of `f_val`.
      validation_steps: Number of steps to run validation for (only if doing
        validation from data tensors). Ignored with default value of `None`.
      do_validation: Boolean value indicating whether we should do validation.

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

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

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

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

    if step_index == 0 and not callback_metrics:
      out_labels = model.metrics_names
      if do_validation:
        callback_metrics = copy.copy(out_labels) + [
            'val_' + n for n in out_labels
        ]
      else:
        callback_metrics = copy.copy(out_labels)
      callbacks.set_params({
          'epochs': epochs,
          'steps': steps_per_epoch,
          'verbose': verbose,
          'do_validation': do_validation,
          'metrics': callback_metrics or [],
      })

#.........这里部分代码省略.........

def test_on_batch(model,
                  inputs,
                  targets,
                  sample_weights=None,
                  reset_metrics=True,
                  output_loss_metrics=None):
  """Calculates the loss for one input batch.

  Arguments:
      model: Model whose loss has to be calculated.
      inputs: Input batch data.
      targets: Target batch data.
      sample_weights: Sample weight batch data.
      reset_metrics: If `True`, the metrics returned will be only for this
        batch. If `False`, the metrics will be statefully accumulated across
        batches.
      output_loss_metrics: List of metrics that are used to aggregated output
        loss values.

  Returns:
      total loss, loss and metrics associated with each output.
  """
  if isinstance(inputs, collections.Sequence):
    if len(inputs) and tensor_util.is_tensor(inputs[0]):
      inputs = training_utils.cast_if_floating_dtype(inputs)
      targets = training_utils.cast_if_floating_dtype(targets)
    else:
      inputs = training_utils.cast_if_floating_dtype(
          [ops.convert_to_tensor(val) for val in inputs])
      targets = training_utils.cast_if_floating_dtype(
          [ops.convert_to_tensor(val) for val in targets])
  if sample_weights:
    sample_weights = [
        training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
        if val is not None else None for val in sample_weights
    ]
  outs, total_loss, output_losses, aggregated_output_losses, masks = (
      _model_loss(
          model,
          inputs,
          targets,
          sample_weights=sample_weights,
          training=False,
          output_loss_metrics=output_loss_metrics))
  if not isinstance(outs, list):
    outs = [outs]
  metrics_results = _eager_metrics_fn(
      model,
      outs,
      targets,
      sample_weights=sample_weights,
      masks=masks,
      return_stateful_result=not reset_metrics)
  total_loss = nest.flatten(total_loss)
  if reset_metrics:
    final_output_losses = output_losses
  else:
    final_output_losses = aggregated_output_losses
  results = total_loss + final_output_losses + metrics_results

  return [tensor_util.constant_value(v) for v in results]