Python generic_utils.Progbar类代码示例

 def on_epoch_begin(self, epoch, logs=None):
   if self.verbose:
     print('Epoch %d/%d' % (epoch + 1, self.epochs))
     if self.use_steps:
       target = self.params['steps']
     else:
       target = self.params['samples']
     self.target = target
     self.progbar = Progbar(
         target=self.target,
         verbose=self.verbose,
         stateful_metrics=self.stateful_metrics)
   self.seen = 0

def predict_generator(model,
                      generator,
                      steps=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      verbose=0):
  """See docstring for `Model.predict_generator`."""
  if not context.executing_eagerly():
    model._make_predict_function()

  steps_done = 0
  all_outs = []
  is_sequence = isinstance(generator, Sequence)
  if not is_sequence and use_multiprocessing and workers > 1:
    logging.warning(
        UserWarning('Using a generator with `use_multiprocessing=True`'
                    ' and multiple workers may duplicate your data.'
                    ' Please consider using the`keras.utils.Sequence'
                    ' class.'))
  if steps is None:
    if is_sequence:
      steps = len(generator)
    else:
      raise ValueError('`steps=None` is only valid for a generator'
                       ' based on the `keras.utils.Sequence` class.'
                       ' Please specify `steps` or use the'
                       ' `keras.utils.Sequence` class.')
  enqueuer = None

  try:
    if workers > 0:
      if is_sequence:
        enqueuer = OrderedEnqueuer(
            generator, use_multiprocessing=use_multiprocessing)
      else:
        enqueuer = GeneratorEnqueuer(
            generator,
            use_multiprocessing=use_multiprocessing)
      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
      output_generator = enqueuer.get()
    else:
      if is_sequence:
        output_generator = iter_sequence_infinite(generator)
      else:
        output_generator = generator

    if verbose == 1:
      progbar = Progbar(target=steps)

    while steps_done < steps:
      generator_output = next(output_generator)
      if isinstance(generator_output, tuple):
        # Compatibility with the generators
        # used for training.
        if len(generator_output) == 2:
          x, _ = generator_output
        elif len(generator_output) == 3:
          x, _, _ = generator_output
        else:
          raise ValueError('Output of generator should be '
                           'a tuple `(x, y, sample_weight)` '
                           'or `(x, y)`. Found: ' + str(generator_output))
      else:
        # Assumes a generator that only
        # yields inputs (not targets and sample weights).
        x = generator_output

      outs = model.predict_on_batch(x)
      if not isinstance(outs, list):
        outs = [outs]

      if not all_outs:
        for out in outs:
          all_outs.append([])

      for i, out in enumerate(outs):
        all_outs[i].append(out)
      steps_done += 1
      if verbose == 1:
        progbar.update(steps_done)

  finally:
    if enqueuer is not None:
      enqueuer.stop()

  if len(all_outs) == 1:
    if steps_done == 1:
      return all_outs[0][0]
    else:
      return np.concatenate(all_outs[0])
  if steps_done == 1:
    return [out[0] for out in all_outs]
  else:
    return [np.concatenate(out) for out in all_outs]

def evaluate_generator(model,
                       generator,
                       steps=None,
                       max_queue_size=10,
                       workers=1,
                       use_multiprocessing=False,
                       verbose=0):
  """See docstring for `Model.evaluate_generator`."""
  if not context.executing_eagerly():
    model._make_test_function()

  if hasattr(model, 'metrics'):
    for m in model.stateful_metric_functions:
      m.reset_states()

  steps_done = 0
  all_outs = []
  batch_sizes = []
  is_sequence = isinstance(generator, Sequence)
  if not is_sequence and use_multiprocessing and workers > 1:
    logging.warning(
        UserWarning('Using a generator with `use_multiprocessing=True`'
                    ' and multiple workers may duplicate your data.'
                    ' Please consider using the`keras.utils.Sequence'
                    ' class.'))
  if steps is None:
    if is_sequence:
      steps = len(generator)
    else:
      raise ValueError('`steps=None` is only valid for a generator'
                       ' based on the `keras.utils.Sequence` class.'
                       ' Please specify `steps` or use the'
                       ' `keras.utils.Sequence` class.')
  enqueuer = None

  try:
    if workers > 0:
      if is_sequence:
        enqueuer = OrderedEnqueuer(
            generator, use_multiprocessing=use_multiprocessing)
      else:
        enqueuer = GeneratorEnqueuer(
            generator,
            use_multiprocessing=use_multiprocessing)
      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
      output_generator = enqueuer.get()
    else:
      if is_sequence:
        output_generator = iter_sequence_infinite(generator)
      else:
        output_generator = generator

    if verbose == 1:
      progbar = Progbar(target=steps)

    while steps_done < steps:
      generator_output = next(output_generator)
      if not hasattr(generator_output, '__len__'):
        raise ValueError('Output of generator should be a tuple '
                         '(x, y, sample_weight) '
                         'or (x, y). Found: ' + str(generator_output))
      if len(generator_output) == 2:
        x, y = generator_output
        sample_weight = None
      elif len(generator_output) == 3:
        x, y, sample_weight = generator_output
      else:
        raise ValueError('Output of generator should be a tuple '
                         '(x, y, sample_weight) '
                         'or (x, y). Found: ' + str(generator_output))
      outs = model.test_on_batch(x, y, sample_weight=sample_weight)

      if isinstance(x, list):
        batch_size = x[0].shape[0]
      elif isinstance(x, dict):
        batch_size = list(x.values())[0].shape[0]
      else:
        batch_size = x.shape[0]
      if batch_size == 0:
        raise ValueError('Received an empty batch. '
                         'Batches should at least contain one item.')
      all_outs.append(outs)

      steps_done += 1
      batch_sizes.append(batch_size)
      if verbose == 1:
        progbar.update(steps_done)

  finally:
    if enqueuer is not None:
      enqueuer.stop()

  if not isinstance(outs, list):
    return np.average(np.asarray(all_outs), weights=batch_sizes)
  else:
    averages = [float(all_outs[-1][0])]  # index 0 = 'loss'
    averages.extend([
        np.average([out[i]
                    for out in all_outs], weights=batch_sizes)
        for i in range(1, len(outs))
#.........这里部分代码省略.........

def _experimental_predict_loop(model, iterator, verbose=0, steps=None):
  """Predict loop for predicting with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring `_predict_loop` finished.
          Ignored with the default value of `None`.

  Returns:
      Array of predictions (if the model has a single output)
      or list of arrays of predictions
      (if the model has multiple outputs).
  """
  current_strategy = model._distribution_strategy
  K.get_session().run(current_strategy.initialize())

  # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
  K.set_learning_phase(0)

  def _per_device_predict_function(model):
    model._make_predict_function()
    return (model.predict_function.inputs,
            model.predict_function.outputs,
            model.predict_function.updates_op,
            model.predict_function.session_kwargs)

  def step_fn(ctx, *inputs):
    """Clones the model and calls make_predict_function."""

    # TODO(priyag, sourabhbajaj): The model gets cloned every time
    # fit/test/predict is called. We should look into caching this keyed on
    # input shapes.
    clone_model_on_replicas(
        model,
        current_strategy,
        make_callback_model=False,
        inputs=inputs,
        mode=_Mode.PREDICT)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_predict_function, args=(model._grouped_model_predict,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_predict_function',
        **all_session_args)

    for label, output in zip(model.output_names, combined_fn.outputs):
      ctx.set_last_step_output(label, output)

    return combined_fn.updates_op

  # Add initial dummy values for outputs.
  initial_loop_values = {}
  batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
  for name, tensor in zip(model.output_names, model.outputs):
    # TODO(priyag): This is a workaround as we do not know the batch dimension
    # of the model's output at this point.
    shape = tensor_shape.TensorShape(tensor.shape.dims)
    shape.dims = [batch_dimension] + shape.dims[1:]
    initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)

  with current_strategy.scope():
    # TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
    ctx = current_strategy.run_steps_on_dataset(
        step_fn, iterator, iterations=1,
        initial_loop_values=initial_loop_values)

  predict_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model_predict)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  assert steps is not None
  # Since we do not know how many samples we will see, we cannot pre-allocate
  # the returned Numpy arrays. Instead, we store one array per batch seen
  # and concatenate them upon returning.
  unconcatenated_outs = [[] for _ in model.outputs]
  for step in range(steps):
    _, batch_outs = K.get_session().run([predict_op, output_tensors])
    # TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
    for i, label in enumerate(model.output_names):
#.........这里部分代码省略.........

def predict_loop(model, iterator, verbose=0, steps=None):
  """Predict loop for predicting with DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring `_predict_loop` finished.
          Ignored with the default value of `None`.

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

  # TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
  if current_strategy.__class__.__name__ == 'TPUStrategy':
    return _experimental_predict_loop(model, iterator, verbose, steps)

  if not model._grouped_model:
    clone_model_on_replicas(model, current_strategy)

  def _per_device_predict_function(model):
    model._make_predict_function()
    return (model.predict_function.inputs,
            model.predict_function.outputs,
            model.predict_function.updates_op,
            model.predict_function.session_kwargs)

  inputs, _, _ = _get_input_from_iterator(iterator, model)
  with current_strategy.scope():
    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_predict_function, args=(model._grouped_model,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    dataset_inputs = distributed_training_utils.flatten_perdevice_values(
        current_strategy, inputs)

    distributed_predict_function = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_predict_function',
        **all_session_args)

    if not isinstance(K.learning_phase(), int):
      ins = dataset_inputs + [0]
    else:
      ins = dataset_inputs

    if verbose == 1:
      progbar = Progbar(target=steps)

    # Copy the weights from the original model to each of the replicated models.
    orig_model_weights = model.get_weights()
    distributed_model = current_strategy.unwrap(model._grouped_model)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

    num_replicas = current_strategy.num_replicas_in_sync
    # Since we do not know how many samples we will see, we cannot
    # pre-allocate the returned Numpy arrays. Instead, we store one array per
    # batch seen and concatenate them upon returning.
    unconcatenated_outs = []
    assert steps is not None
    for step in range(steps):
      batch_outs = distributed_predict_function(ins)
      if not isinstance(batch_outs, list):
        batch_outs = [batch_outs]
      if step == 0:
        # batch_outs gives you the number of model outputs. In the distributed
        # case this will be number of model_outputs * num_replicas.
        for _ in range(len(model.outputs)):
          unconcatenated_outs.append([])
      for i in range(len(model.outputs)):
        nested_outs = batch_outs[i * num_replicas:
                                 i * num_replicas + num_replicas]
        outs = nest.flatten(nested_outs)
        unconcatenated_outs[i].extend(outs)
      if verbose >= 1:
        progbar.update(step + 1)
    if len(unconcatenated_outs) == 1:
      return np.concatenate(unconcatenated_outs[0], axis=0)
    return [
        np.concatenate(unconcatenated_outs[i], axis=0)
        for i in range(len(unconcatenated_outs))
    ]

def _experimental_test_loop(model, iterator, verbose=0, steps=None,
                            initialize_finalize_strategy=True):
  """Test loop for evaluating with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.
      initialize_finalize_strategy: Should the strategy initialize and finalize
          functions be called.

  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 outputs.
  """
  current_strategy = model._distribution_strategy
  if initialize_finalize_strategy:
    K.get_session().run(current_strategy.initialize())

  def _per_device_eval_function(model):
    model._make_eval_function()
    return (model._eval_function.inputs, model._eval_function.outputs,
            model._eval_function.updates_op,
            model._eval_function.session_kwargs)

  # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
  K.set_learning_phase(0)

  def step_fn(ctx, inputs, targets):
    """Clones the model and calls make_eval_function."""
    # TODO(priyag, sourabhbajaj): The model gets cloned every time
    # fit/test/predict is called. We should look into caching this keyed on
    # input shapes.
    clone_model_on_replicas(
        model,
        current_strategy,
        make_callback_model=False,
        inputs=inputs,
        targets=targets,
        mode=_Mode.TEST)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_eval_function, args=(model._grouped_model_test,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_test_function',
        **all_session_args)

    for label, output in zip(model.metrics_names, combined_fn.outputs):
      if label == 'loss':
        aggregation = distribute_lib.get_loss_reduction()
      else:
        # We aggregate all other metrics using mean for now. This is temporary
        # workaround until new metrics are in place.
        aggregation = variable_scope.VariableAggregation.MEAN
      ctx.set_last_step_output(label, output, aggregation)

    return combined_fn.updates_op

  # Add initial dummy values for loss and other metric tensors.
  initial_loop_values = {}
  initial_loop_values['loss'] = constant_op.constant(1e7)
  for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
    initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)

  with current_strategy.scope():
    # TODO(priyag): Use steps_per_run when we use new metrics as they will
    # allow handling metric computation at each step using variables.
    ctx = current_strategy.run_steps_on_dataset(
        step_fn, iterator, iterations=1,
        initial_loop_values=initial_loop_values)

  test_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model_test)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  assert steps is not None
  outs = [0.] * len(model.metrics_names)
#.........这里部分代码省略.........

def test_loop(model, iterator, verbose=0, steps=None):
  """Test loop for evaluating with DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      iterator: Iterator for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.

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

  # TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
  if current_strategy.__class__.__name__ == 'TPUStrategy':
    return _experimental_test_loop(model, iterator, verbose, steps)

  if not model._grouped_model:
    clone_model_on_replicas(model, current_strategy)

  def _per_device_eval_function(model):
    model._make_eval_function()
    return (model._eval_function.inputs, model._eval_function.outputs,
            model._eval_function.updates_op,
            model._eval_function.session_kwargs)

  inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
  with current_strategy.scope():
    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_replica(
         _per_device_eval_function, args=(model._grouped_model,))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args, with_loss_tensor=True)

    dataset_inputs = distributed_training_utils.flatten_perdevice_values(
        current_strategy, inputs)
    dataset_targets = distributed_training_utils.flatten_perdevice_values(
        current_strategy, targets)

    distributed_test_function = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_test_function',
        **all_session_args)

    # We need to set sample_weights to None since there are sample weight
    # placeholders that are created with default values.
    sample_weights = [None for _ in range(
        len(model.outputs) * current_strategy.num_replicas_in_sync)]
    if not isinstance(K.learning_phase(), int):
      ins = dataset_inputs + dataset_targets + sample_weights + [0]
    else:
      ins = dataset_inputs + dataset_targets

    for m in model.stateful_metric_functions:
      m.reset_states()

    outs = []
    if verbose == 1:
      progbar = Progbar(target=steps)

    # Copy the weights from the original model to each of the replicated models.
    orig_model_weights = model.get_weights()
    distributed_model = current_strategy.unwrap(model._grouped_model)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

    assert steps is not None
    for step in range(steps):
      batch_outs = distributed_test_function(ins)
      if isinstance(batch_outs, list):
        if step == 0:
          outs = [0.] * len(batch_outs)
        outs[0] += batch_outs[0]  # index 0 = 'loss'
        outs[1:] = batch_outs[1:]
      else:
        if step == 0:
          outs.append(0.)
        outs[0] += batch_outs  # index 0 = 'loss'
      if verbose >= 1:
        progbar.update(step + 1)
    outs[0] /= steps  # index 0 = 'loss'

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

def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
  """Abstract method to loop over some data in batches.

  Arguments:
      model: Keras Model instance.
      inputs: list of tensors to be fed to `f`.
      batch_size: integer batch size.
      verbose: verbosity mode.
      steps: Total number of steps (batches of samples)
          before declaring `_predict_loop` finished.
          Ignored with the default value of `None`.

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

  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
    ins = inputs + [0]
  else:
    ins = inputs

  num_samples = training_utils.check_num_samples(
      inputs, batch_size, steps, 'steps')
  if verbose == 1:
    if steps is not None:
      progbar = Progbar(target=steps)
    else:
      progbar = Progbar(target=num_samples)

  indices_for_conversion_to_dense = []
  for i in range(len(model._feed_inputs)):
    if (issparse is not None and issparse(inputs[i]) and
        not K.is_sparse(model._feed_inputs[i])):
      indices_for_conversion_to_dense.append(i)

  if steps is not None:
    # Step-based predictions.
    # Since we do not know how many samples
    # we will see, we cannot pre-allocate
    # the returned Numpy arrays.
    # Instead, we store one array per batch seen
    # and concatenate them upon returning.
    unconcatenated_outs = []
    for step in range(steps):
      batch_outs = f(ins)
      if not isinstance(batch_outs, list):
        batch_outs = [batch_outs]
      if step == 0:
        for batch_out in batch_outs:
          unconcatenated_outs.append([])
      for i, batch_out in enumerate(batch_outs):
        unconcatenated_outs[i].append(batch_out)
      if verbose == 1:
        progbar.update(step + 1)
    if len(unconcatenated_outs) == 1:
      return np.concatenate(unconcatenated_outs[0], axis=0)
    return [
        np.concatenate(unconcatenated_outs[i], axis=0)
        for i in range(len(unconcatenated_outs))
    ]
  else:
    # Sample-based predictions.
    outs = []
    batches = make_batches(num_samples, batch_size)
    index_array = np.arange(num_samples)
    for batch_index, (batch_start, batch_end) in enumerate(batches):
      batch_ids = index_array[batch_start:batch_end]
      if ins and isinstance(ins[-1], int):
        # Do not slice the training phase flag.
        ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
      else:
        ins_batch = slice_arrays(ins, batch_ids)
      for i in indices_for_conversion_to_dense:
        ins_batch[i] = ins_batch[i].toarray()

      batch_outs = f(ins_batch)
      if not isinstance(batch_outs, list):
        batch_outs = [batch_outs]
      if batch_index == 0:
        # Pre-allocate the results arrays.
        for batch_out in batch_outs:
          shape = (num_samples,) + batch_out.shape[1:]
          outs.append(np.zeros(shape, dtype=batch_out.dtype))
      for i, batch_out in enumerate(batch_outs):
        outs[i][batch_start:batch_end] = batch_out
      if verbose == 1:
        progbar.update(batch_end)
    if len(outs) == 1:
      return outs[0]
    return outs

class ProgbarLogger(Callback):
  """Callback that prints metrics to stdout.

  Arguments:
      count_mode: One of "steps" or "samples".
          Whether the progress bar should
          count samples seen or steps (batches) seen.
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over an epoch.
          Metrics in this list will be logged as-is.
          All others will be averaged over time (e.g. loss, etc).

  Raises:
      ValueError: In case of invalid `count_mode`.
  """

  def __init__(self, count_mode='samples', stateful_metrics=None):
    super(ProgbarLogger, self).__init__()
    if count_mode == 'samples':
      self.use_steps = False
    elif count_mode == 'steps':
      self.use_steps = True
    else:
      raise ValueError('Unknown `count_mode`: ' + str(count_mode))
    self.stateful_metrics = set(stateful_metrics or [])

  def on_train_begin(self, logs=None):
    self.verbose = self.params['verbose']
    self.epochs = self.params['epochs']

  def on_epoch_begin(self, epoch, logs=None):
    if self.verbose:
      print('Epoch %d/%d' % (epoch + 1, self.epochs))
      if self.use_steps:
        target = self.params['steps']
      else:
        target = self.params['samples']
      self.target = target
      self.progbar = Progbar(
          target=self.target,
          verbose=self.verbose,
          stateful_metrics=self.stateful_metrics)
    self.seen = 0

  def on_batch_begin(self, batch, logs=None):
    if self.seen < self.target:
      self.log_values = []

  def on_batch_end(self, batch, logs=None):
    logs = logs or {}
    batch_size = logs.get('size', 0)
    if self.use_steps:
      self.seen += 1
    else:
      self.seen += batch_size

    for k in self.params['metrics']:
      if k in logs:
        self.log_values.append((k, logs[k]))

    # Skip progbar update for the last batch;
    # will be handled by on_epoch_end.
    if self.verbose and self.seen < self.target:
      self.progbar.update(self.seen, self.log_values)

  def on_epoch_end(self, epoch, logs=None):
    logs = logs or {}
    for k in self.params['metrics']:
      if k in logs:
        self.log_values.append((k, logs[k]))
    if self.verbose:
      self.progbar.update(self.seen, self.log_values)

def experimental_tpu_test_loop(model,
                               dataset,
                               verbose=0,
                               steps=None,
                               callbacks=None):
  """Test loop for evaluating with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      dataset: Dataset for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.
      callbacks: List of callbacks to be called during training

  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 outputs.
  """
  mode = ModeKeys.TEST
  current_strategy = model._distribution_strategy
  iterator = distributed_training_utils.get_iterator(dataset,
                                                     current_strategy)
  steps = training_utils.infer_steps_for_dataset(dataset, steps,
                                                 steps_name='steps')

  scope = distributed_training_utils.distributed_scope(
      strategy=current_strategy, learning_phase=0)
  scope.__enter__()

  out_labels = model.metrics_names
  step_fn = _make_step_fn(model, ModeKeys.TEST, current_strategy, out_labels)

  # Add initial dummy values for loss and other metric tensors.
  initial_loop_values = {}
  initial_loop_values['loss'] = constant_op.constant(1e7)
  for name in model.metrics_names[1:]:
    tensor = model._all_stateful_metrics_tensors[name]
    initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)

  # TODO(priyag): Use steps_per_run when we use new metrics as they will
  # allow handling metric computation at each step using variables.
  ctx = current_strategy.extended.experimental_run_steps_on_iterator(
      step_fn, iterator, iterations=1,
      initial_loop_values=initial_loop_values)

  test_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  if model._compile_distribution:
    distributed_training_utils._copy_weights_to_distributed_model(model, mode)

  distributed_training_utils._reset_metrics(model)

  callbacks = cbks.configure_callbacks(
      callbacks,
      model,
      do_validation=False,
      epochs=1,
      steps_per_epoch=steps,
      verbose=verbose,
      count_mode='steps',
      mode=ModeKeys.TEST)
  callbacks._call_begin_hook(mode)

  outs = [0.] * len(model.metrics_names)
  if steps is not None:
    target_steps = steps
  else:
    target_steps = np.inf

  current_step = 0
  while current_step < target_steps:
    batch_logs = {'batch': current_step, 'size': 1}
    callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
    try:
      _, batch_outs = K.batch_get_value([test_op, output_tensors])
    except errors.OutOfRangeError:
      if steps is not None:
        warning_msg = 'Make sure that your dataset can generate at least '
        '`steps` batches (in this case, {} batches).'.format(steps)
      else:
        warning_msg = 'Number of steps ran: {} steps'.format(current_step)

      logging.warning('Your dataset iterator ran out of data; '
                      'interrupting evaluation. ' + warning_msg)
      target_steps = current_step
      break
    for i, label in enumerate(model.metrics_names):
      if i == 0:
        # Loss is stateless metrics.
        outs[i] += batch_outs[label]
      else:
        # For all stateful metrics, the aggregation is handled by mirrored vars.
#.........这里部分代码省略.........

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

  Arguments:
      model: Keras Model instance.
      inputs: list of tensors to be fed to `f`.
      verbose: verbosity mode.
      steps: Total number of steps (batches of samples)
          before declaring `_predict_loop` finished.
          Ignored with the default value of `None`.

  Returns:
      Array of predictions (if the model has a single output)
      or list of arrays of predictions
      (if the model has multiple outputs).
  """
  current_strategy = model._distribution_strategy
  def _per_device_predict_function(model):
    model._make_predict_function()
    return (model.predict_function.inputs,
            model.predict_function.outputs,
            model.predict_function.updates_op,
            model.predict_function.session_kwargs)

  with current_strategy.scope():
    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_tower(
         _per_device_predict_function, model._grouped_model)

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    dataset_inputs = distributed_training_utils.flatten_perdevice_values(
        current_strategy, inputs)

  distributed_predict_function = K.Function(
      all_inputs, all_outputs,
      updates=all_updates,
      name='distributed_predict_function',
      **all_session_args)

  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
    ins = dataset_inputs + [0]
  else:
    ins = dataset_inputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  if steps is not None:
    # Since we do not know how many samples we will see, we cannot pre-allocate
    # the returned Numpy arrays. Instead, we store one array per batch seen
    # and concatenate them upon returning.
    unconcatenated_outs = []
    for step in range(steps):
      batch_outs = distributed_predict_function(ins)
      if not isinstance(batch_outs, list):
        batch_outs = [batch_outs]
      if step == 0:
        for _ in batch_outs:
          unconcatenated_outs.append([])
      for i, batch_out in enumerate(batch_outs):
        unconcatenated_outs[i].append(batch_out)
      if verbose == 1:
        progbar.update(step + 1)
    if len(unconcatenated_outs) == 1:
      return np.concatenate(unconcatenated_outs[0], axis=0)
    return [
        np.concatenate(unconcatenated_outs[i], axis=0)
        for i in range(len(unconcatenated_outs))
    ]

def test_loop(model, inputs, targets, verbose=0, steps=None):
  """evaluate method to validate a model that uses DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      inputs: List of input arrays.
      targets: List of target arrays.
      verbose: verbosity mode.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.

  Returns:
      Scalar loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.
  """
  current_strategy = model._distribution_strategy
  def _per_device_test_function(model):
    model._make_test_function()
    return (model.test_function.inputs,
            model.test_function.outputs,
            model.test_function.updates_op,
            model.test_function.session_kwargs)

  with current_strategy.scope():
    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.call_for_each_tower(
         _per_device_test_function, model._grouped_model)

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args, with_loss_tensor=True)

    dataset_inputs = distributed_training_utils.flatten_perdevice_values(
        current_strategy, inputs)
    dataset_targets = distributed_training_utils.flatten_perdevice_values(
        current_strategy, targets)

  distributed_test_function = K.Function(
      all_inputs, all_outputs,
      updates=all_updates,
      name='distributed_test_function',
      **all_session_args)

  # We need to set sample_weights to None since there are sample weight
  # placeholders that are created with default values.
  sample_weights = [None for _ in range(len(model.outputs) *
                                        current_strategy.num_towers)]
  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
    ins = dataset_inputs + dataset_targets + sample_weights + [0]
  else:
    ins = dataset_inputs + dataset_targets

  outs = []
  if verbose == 1:
    progbar = Progbar(target=steps)

  # Copy the weights from the original model to each of the replicated models.
  orig_model_weights = model.get_weights()
  with current_strategy.scope():
    distributed_model = current_strategy.unwrap(model._grouped_model)[0]
    distributed_training_utils.set_weights(
        current_strategy, distributed_model, orig_model_weights)

  if steps is not None:
    for step in range(steps):
      batch_outs = distributed_test_function(ins)
      batch_outs = _aggregate_metrics_across_towers(
          len(current_strategy._devices), model.metrics_names, batch_outs)
      if isinstance(batch_outs, list):
        if step == 0:
          for _ in enumerate(batch_outs):
            outs.append(0.)
        for i, batch_out in enumerate(batch_outs):
          outs[i] += batch_out
      else:
        if step == 0:
          outs.append(0.)
        outs[0] += batch_outs
      if verbose == 1:
        progbar.update(step + 1)
    for i in range(len(outs)):
      outs[i] /= steps

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

def experimental_tpu_test_loop(model,
                               dataset,
                               verbose=0,
                               steps=None,
                               callbacks=None):
  """Test loop for evaluating with TPU DistributionStrategy.

  Arguments:
      model: Keras Model instance.
      dataset: Dataset for input data.
      verbose: Integer, Verbosity mode 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.
      callbacks: List of callbacks to be called during training

  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 outputs.
  """
  mode = ModeKeys.TEST
  current_strategy = model._distribution_strategy
  iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
  scope = distributed_training_utils.distributed_scope(
      strategy=current_strategy, learning_phase=0)
  scope.__enter__()

  def _per_device_eval_function(model):
    model._make_eval_function()
    return (model._eval_function.inputs, model._eval_function.outputs,
            model._eval_function.updates_op,
            model._eval_function.session_kwargs)

  def step_fn(ctx, inputs):
    """Clones the model and calls make_eval_function."""
    inputs, targets = inputs
    if model._compile_distribution:
      distributed_training_utils.clone_model_on_replicas(
          model, current_strategy, mode=mode, inputs=inputs, targets=targets)
    else:
      distributed_training_utils._build_distributed_network(
          model, current_strategy, mode, inputs, targets)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.extended.call_for_each_replica(
         _per_device_eval_function,
         args=(distributed_training_utils.get_distributed_model(
             model, ModeKeys.TEST),))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_test_function',
        **all_session_args)

    for label, output in zip(model.metrics_names, combined_fn.outputs):
      if label == 'loss':
        reduce_op = ds_reduce_util.ReduceOp.SUM
      else:
        # We reduce all other metrics using mean for now. This is temporary
        # workaround until new metrics are in place.
        reduce_op = ds_reduce_util.ReduceOp.MEAN
      ctx.set_last_step_output(label, output, reduce_op)

    return combined_fn.updates_op

  # Add initial dummy values for loss and other metric tensors.
  initial_loop_values = {}
  initial_loop_values['loss'] = constant_op.constant(1e7)
  for name in model.metrics_names[1:]:
    tensor = model._all_stateful_metrics_tensors[name]
    initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)

  # TODO(priyag): Use steps_per_run when we use new metrics as they will
  # allow handling metric computation at each step using variables.
  ctx = current_strategy.extended.experimental_run_steps_on_iterator(
      step_fn, iterator, iterations=1,
      initial_loop_values=initial_loop_values)

  test_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  if model._compile_distribution:
    distributed_training_utils._copy_weights_to_distributed_model(model, mode)

  distributed_training_utils._reset_metrics(model)

  callbacks = cbks.configure_callbacks(
      callbacks,
      model,
#.........这里部分代码省略.........

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

  Arguments:
      model: Keras Model instance.
      inputs: List of input arrays.
      targets: List of target arrays.
      sample_weights: Optional list of sample weight arrays.
      batch_size: integer batch size or `None`.
      verbose: verbosity mode.
      steps: Total number of steps (batches of samples)
          before declaring predictions finished.
          Ignored with the default value of `None`.

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

  sample_weights = sample_weights or []
  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
    ins = inputs + targets + sample_weights + [0]
  else:
    ins = inputs + targets + sample_weights

  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 = []

  num_samples = training_utils.check_num_samples(
      ins, batch_size, steps, 'steps')
  outs = []
  if verbose == 1:
    if steps is not None:
      progbar = Progbar(target=steps)
    else:
      progbar = Progbar(target=num_samples)

  # To prevent a slowdown, we find beforehand the arrays that need conversion.
  feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
  indices_for_conversion_to_dense = []
  for i in range(len(feed)):
    if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
      indices_for_conversion_to_dense.append(i)

  if steps is not None:
    for step in range(steps):
      batch_outs = f(ins)
      if isinstance(batch_outs, list):
        if step == 0:
          for _ in enumerate(batch_outs):
            outs.append(0.)
        for i, batch_out in enumerate(batch_outs):
          if i in stateful_metric_indices:
            outs[i] = batch_out
          else:
            outs[i] += batch_out
      else:
        if step == 0:
          outs.append(0.)
        outs[0] += batch_outs
      if verbose == 1:
        progbar.update(step + 1)
    for i in range(len(outs)):
      if i not in stateful_metric_indices:
        outs[i] /= steps
  else:
    batches = make_batches(num_samples, batch_size)
    index_array = np.arange(num_samples)
    for batch_index, (batch_start, batch_end) in enumerate(batches):
      batch_ids = index_array[batch_start:batch_end]
      if isinstance(ins[-1], int):
        # Do not slice the training phase flag.
        ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
      else:
        ins_batch = slice_arrays(ins, batch_ids)
      for i in indices_for_conversion_to_dense:
        ins_batch[i] = ins_batch[i].toarray()

      batch_outs = f(ins_batch)

      if isinstance(batch_outs, list):
        if batch_index == 0:
          outs.extend([0.] * len(batch_outs))
#.........这里部分代码省略.........

#.........这里部分代码省略.........
    """Clones the model and calls make_predict_function."""
    if model._compile_distribution:
      distributed_training_utils.clone_model_on_replicas(
          model, current_strategy, mode, inputs=inputs)
    else:
      distributed_training_utils._build_distributed_network(
          model, current_strategy, mode, inputs)

    (grouped_inputs, grouped_outputs, grouped_updates,
     grouped_session_args) = current_strategy.extended.call_for_each_replica(
         _per_device_predict_function,
         args=(distributed_training_utils.get_distributed_model(
             model, ModeKeys.PREDICT),))

    (all_inputs, all_outputs, all_updates,
     all_session_args) = distributed_training_utils.unwrap_values(
         current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
         grouped_session_args)

    combined_fn = K.function(
        all_inputs, all_outputs,
        updates=all_updates,
        name='distributed_predict_function',
        **all_session_args)

    for label, output in zip(model.output_names, combined_fn.outputs):
      ctx.set_last_step_output(label, output)

    return combined_fn.updates_op

  # Add initial dummy values for outputs.
  initial_loop_values = {}
  batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
  for name, tensor in zip(model.output_names, model.outputs):
    # TODO(priyag): This is a workaround as we do not know the batch dimension
    # of the model's output at this point.
    shape = tensor_shape.TensorShape(tensor.shape.dims)
    shape.dims = [batch_dimension] + shape.dims[1:]
    initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)

  # TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
  ctx = current_strategy.extended.experimental_run_steps_on_iterator(
      step_fn, iterator, iterations=1,
      initial_loop_values=initial_loop_values)

  predict_op = ctx.run_op
  output_tensors = ctx.last_step_outputs

  if verbose == 1:
    progbar = Progbar(target=steps)

  if model._compile_distribution:
    distributed_training_utils._copy_weights_to_distributed_model(model, mode)

  distributed_training_utils._reset_metrics(model)

  callbacks = cbks.configure_callbacks(
      callbacks,
      model,
      do_validation=False,
      epochs=1,
      steps_per_epoch=steps,
      verbose=verbose,
      count_mode='steps',
      mode=mode)
  callbacks._call_begin_hook(mode)

  assert steps is not None
  # Since we do not know how many samples we will see, we cannot pre-allocate
  # the returned Numpy arrays. Instead, we store one array per batch seen
  # and concatenate them upon returning.
  unconcatenated_outs = [[] for _ in model.outputs]
  for step in range(steps):
    batch_logs = {'batch': step, 'size': 1}
    callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
    _, batch_outs = K.get_session().run([predict_op, output_tensors])
    # TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
    for i, label in enumerate(model.output_names):
      unconcatenated_outs[i].extend(batch_outs[label])
    batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
    callbacks._call_batch_hook(mode, 'end', step, batch_logs)
    if verbose >= 1:
      progbar.update(step + 1)

  callbacks._call_end_hook(mode)

  scope.__exit__(None, None, None)

  if len(unconcatenated_outs) == 1:
    prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
  else:
    prediction_result = [
        np.concatenate(unconcatenated_outs[i], axis=0)
        for i in range(len(unconcatenated_outs))
    ]

  if padding_handler:
    prediction_result = padding_handler.apply_mask(prediction_result)

  return prediction_result