基于Keras的格式化输出Loss实现方式

在win7 64位，Anaconda安装的Python3.6.1下安装的TensorFlow与Keras，Keras的backend为TensorFlow。在运行Mask R-CNN时，在进行调试时想知道PyCharm (Python IDE)底部窗口输出的Loss格式是在哪里定义的，如下图红框中所示：

图1 训练过程的Loss格式化输出

在上图红框中，Loss的输出格式是在哪里定义的呢？有一点是明确的，即上图红框中的内容是在训练的时候输出的。那么先来看一下Mask R-CNN的训练过程。Keras以Numpy数组作为输入数据和标签的数据类型。训练模型一般使用 fit 函数。然而由于Mask R-CNN训练数据巨大，不能一次性全部载入，否则太消耗内存。于是采用生成器的方式一次载入一个batch的数据，而且是在用到这个batch的数据才开始载入的，那么它的训练函数如下：

 self.keras_model.fit_generator(
   train_generator,
   initial_epoch=self.epoch,
   epochs=epochs,
   steps_per_epoch=self.config.STEPS_PER_EPOCH,
   callbacks=callbacks,
   validation_data=val_generator,
   validation_steps=self.config.VALIDATION_STEPS,
   max_queue_size=100,
   workers=workers,
   use_multiprocessing=False,
  )

这里训练模型的函数相应的为 fit_generator 函数。注意其中的参数callbacks=callbacks，这个参数在输出红框中的内容起到了关键性的作用。下面看一下callbacks的值：

# Callbacks
  callbacks = [
   keras.callbacks.TensorBoard(log_dir=self.log_dir,
          histogram_freq=0, write_graph=True, write_images=False),
   keras.callbacks.ModelCheckpoint(self.checkpoint_path,
           verbose=0, save_weights_only=True),
  ]

在输出红框中的内容所需的数据均保存在self.log_dir下。然后调试进入self.keras_model.fit_generator函数，进入keras,legacy.interfaces的legacy_support(func)函数，如下所示：

 def legacy_support(func):
  @six.wraps(func)
  def wrapper(*args, **kwargs):
   if object_type == 'class':
    object_name = args[0].__class__.__name__
   else:
    object_name = func.__name__
   if preprocessor:
    args, kwargs, converted = preprocessor(args, kwargs)
   else:
    converted = []
   if check_positional_args:
    if len(args) > len(allowed_positional_args) + 1:
     raise TypeError('`' + object_name +
         '` can accept only ' +
         str(len(allowed_positional_args)) +
         ' positional arguments ' +
         str(tuple(allowed_positional_args)) +
         ', but you passed the following '
         'positional arguments: ' +
         str(list(args[1:])))
   for key in value_conversions:
    if key in kwargs:
     old_value = kwargs[key]
     if old_value in value_conversions[key]:
      kwargs[key] = value_conversions[key][old_value]
   for old_name, new_name in conversions:
    if old_name in kwargs:
     value = kwargs.pop(old_name)
     if new_name in kwargs:
      raise_duplicate_arg_error(old_name, new_name)
     kwargs[new_name] = value
     converted.append((new_name, old_name))
   if converted:
    signature = '`' + object_name + '('
    for i, value in enumerate(args[1:]):
     if isinstance(value, six.string_types):
      signature += '"' + value + '"'
     else:
      if isinstance(value, np.ndarray):
       str_val = 'array'
      else:
       str_val = str(value)
      if len(str_val) > 10:
       str_val = str_val[:10] + '...'
      signature += str_val
     if i < len(args[1:]) - 1 or kwargs:
      signature += ', '
    for i, (name, value) in enumerate(kwargs.items()):
     signature += name + '='
     if isinstance(value, six.string_types):
      signature += '"' + value + '"'
     else:
      if isinstance(value, np.ndarray):
       str_val = 'array'
      else:
       str_val = str(value)
      if len(str_val) > 10:
       str_val = str_val[:10] + '...'
      signature += str_val
     if i < len(kwargs) - 1:
      signature += ', '
    signature += ')`'
    warnings.warn('Update your `' + object_name +
        '` call to the Keras 2 API: ' + signature, stacklevel=2)
   return func(*args, **kwargs)
  wrapper._original_function = func
  return wrapper
 return legacy_support

在上述代码的倒数第4行的return func(*args, **kwargs)处返回func，func为fit_generator函数，现调试进入fit_generator函数，该函数定义在keras.engine.training模块内的fit_generator函数，调试进入函数callbacks.on_epoch_begin(epoch)，如下所示：

# Construct epoch logs.
   epoch_logs = {}
   while epoch < epochs:
    for m in self.stateful_metric_functions:
     m.reset_states()
    callbacks.on_epoch_begin(epoch)

调试进入到callbacks.on_epoch_begin(epoch)函数，进入on_epoch_begin函数，如下所示：

def on_epoch_begin(self, epoch, logs=None):
  """Called at the start of an epoch.
  # Arguments
   epoch: integer, index of epoch.
   logs: dictionary of logs.
  """
  logs = logs or {}
  for callback in self.callbacks:
   callback.on_epoch_begin(epoch, logs)
  self._delta_t_batch = 0.
  self._delta_ts_batch_begin = deque([], maxlen=self.queue_length)
  self._delta_ts_batch_end = deque([], maxlen=self.queue_length)

在上述函数on_epoch_begin中调试进入callback.on_epoch_begin(epoch, logs)函数，转到类ProgbarLogger(Callback)中定义的on_epoch_begin函数，如下所示：

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))
  if stateful_metrics:
   self.stateful_metrics = set(stateful_metrics)
  else:
   self.stateful_metrics = set()
 
 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

在上述代码的

print('Epoch %d/%d' % (epoch + 1, self.epochs))

输出

Epoch 1/40（如红框中所示内容的第一行）。

然后返回到keras.engine.training模块内的fit_generator函数，执行到self.train_on_batch函数，如下所示：

outs = self.train_on_batch(x, y,
     sample_weight=sample_weight,
     class_weight=class_weight)
 
     if not isinstance(outs, list):
      outs = [outs]
     for l, o in zip(out_labels, outs):
      batch_logs[l] = o
 
     callbacks.on_batch_end(batch_index, batch_logs)
 
     batch_index += 1
     steps_done += 1

调试进入上述代码中的callbacks.on_batch_end(batch_index, batch_logs)函数，进入到on_batch_end函数后，该函数的定义如下所示：

 def on_batch_end(self, batch, logs=None):
  """Called at the end of a batch.
  # Arguments
   batch: integer, index of batch within the current epoch.
   logs: dictionary of logs.
  """
  logs = logs or {}
  if not hasattr(self, '_t_enter_batch'):
   self._t_enter_batch = time.time()
  self._delta_t_batch = time.time() - self._t_enter_batch
  t_before_callbacks = time.time()
  for callback in self.callbacks:
   callback.on_batch_end(batch, logs)
  self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
  delta_t_median = np.median(self._delta_ts_batch_end)
  if (self._delta_t_batch > 0. and
   (delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)):
   warnings.warn('Method on_batch_end() is slow compared '
       'to the batch update (%f). Check your callbacks.'
       % delta_t_median)

接着继续调试进入上述代码中的callback.on_batch_end(batch, logs)函数，进入到在类中ProgbarLogger(Callback)定义的on_batch_end函数，如下所示：

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)

然后执行到上述代码的最后一行self.progbar.update(self.seen, self.log_values)，调试进入update函数，该函数定义在模块keras.utils.generic_utils中的类Progbar(object)定义的函数。类的定义及方法如下所示：

class Progbar(object):
 """Displays a progress bar.
 # Arguments
  target: Total number of steps expected, None if unknown.
  width: Progress bar width on screen.
  verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
  stateful_metrics: Iterable of string names of metrics that
   should *not* be averaged over time. Metrics in this list
   will be displayed as-is. All others will be averaged
   by the progbar before display.
  interval: Minimum visual progress update interval (in seconds).
 """
 
 def __init__(self, target, width=30, verbose=1, interval=0.05,
     stateful_metrics=None):
  self.target = target
  self.width = width
  self.verbose = verbose
  self.interval = interval
  if stateful_metrics:
   self.stateful_metrics = set(stateful_metrics)
  else:
   self.stateful_metrics = set()
 
  self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
         sys.stdout.isatty()) or
         'ipykernel' in sys.modules)
  self._total_width = 0
  self._seen_so_far = 0
  self._values = collections.OrderedDict()
  self._start = time.time()
  self._last_update = 0
 
 def update(self, current, values=None):
  """Updates the progress bar.
  # Arguments
   current: Index of current step.
   values: List of tuples:
    `(name, value_for_last_step)`.
    If `name` is in `stateful_metrics`,
    `value_for_last_step` will be displayed as-is.
    Else, an average of the metric over time will be displayed.
  """
  values = values or []
  for k, v in values:
   if k not in self.stateful_metrics:
    if k not in self._values:
     self._values[k] = [v * (current - self._seen_so_far),
          current - self._seen_so_far]
    else:
     self._values[k][0] += v * (current - self._seen_so_far)
     self._values[k][1] += (current - self._seen_so_far)
   else:
    # Stateful metrics output a numeric value. This representation
    # means "take an average from a single value" but keeps the
    # numeric formatting.
    self._values[k] = [v, 1]
  self._seen_so_far = current
 
  now = time.time()
  info = ' - %.0fs' % (now - self._start)
  if self.verbose == 1:
   if (now - self._last_update < self.interval and
     self.target is not None and current < self.target):
    return
 
   prev_total_width = self._total_width
   if self._dynamic_display:
    sys.stdout.write('\b' * prev_total_width)
    sys.stdout.write('\r')
   else:
    sys.stdout.write('\n')
 
   if self.target is not None:
    numdigits = int(np.floor(np.log10(self.target))) + 1
    barstr = '%%%dd/%d [' % (numdigits, self.target)
    bar = barstr % current
    prog = float(current) / self.target
    prog_width = int(self.width * prog)
    if prog_width > 0:
     bar += ('=' * (prog_width - 1))
     if current < self.target:
      bar += '>'
     else:
      bar += '='
    bar += ('.' * (self.width - prog_width))
    bar += ']'
   else:
    bar = '%7d/Unknown' % current
 
   self._total_width = len(bar)
   sys.stdout.write(bar)
 
   if current:
    time_per_unit = (now - self._start) / current
   else:
    time_per_unit = 0
   if self.target is not None and current < self.target:
    eta = time_per_unit * (self.target - current)
    if eta > 3600:
     eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, eta % 60)
    elif eta > 60:
     eta_format = '%d:%02d' % (eta // 60, eta % 60)
    else:
     eta_format = '%ds' % eta
 
    info = ' - ETA: %s' % eta_format
   else:
    if time_per_unit >= 1:
     info += ' %.0fs/step' % time_per_unit
    elif time_per_unit >= 1e-3:
     info += ' %.0fms/step' % (time_per_unit * 1e3)
    else:
     info += ' %.0fus/step' % (time_per_unit * 1e6)
 
   for k in self._values:
    info += ' - %s:' % k
    if isinstance(self._values[k], list):
     avg = np.mean(
      self._values[k][0] / max(1, self._values[k][1]))
     if abs(avg) > 1e-3:
      info += ' %.4f' % avg
     else:
      info += ' %.4e' % avg
    else:
     info += ' %s' % self._values[k]
 
   self._total_width += len(info)
   if prev_total_width > self._total_width:
    info += (' ' * (prev_total_width - self._total_width))
 
   if self.target is not None and current >= self.target:
    info += '\n'
 
   sys.stdout.write(info)
   sys.stdout.flush()
 
  elif self.verbose == 2:
   if self.target is None or current >= self.target:
    for k in self._values:
     info += ' - %s:' % k
     avg = np.mean(
      self._values[k][0] / max(1, self._values[k][1]))
     if avg > 1e-3:
      info += ' %.4f' % avg
     else:
      info += ' %.4e' % avg
    info += '\n'
 
    sys.stdout.write(info)
    sys.stdout.flush()
 
  self._last_update = now
 
 def add(self, n, values=None):
  self.update(self._seen_so_far + n, values)

重点是上述代码中的update(self, current, values=None)函数，在该函数内设置断点，即可调入该函数。下面重点分析上述代码中的几个输出条目：

1. sys.stdout.write('\n') #换行

2. sys.stdout.write('bar') #输出 [..................]，其中bar= [..................]；

3. sys.stdout.write(info) #输出loss格式，其中info='- ETA:...'；

4. sys.stdout.flush() #刷新缓存，立即得到输出。

通过对Mask R-CNN代码的调试分析可知，图1中的红框中的训练过程中的Loss格式化输出是由built-in模块实现的。若想得到类似的格式化输出，关键在self.keras_model.fit_generator函数中传入callbacks参数和callbacks中内容的定义。

以上这篇基于Keras的格式化输出Loss实现方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。