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。