sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
  optimizer=sgd,
  metrics=['accuracy'])

def get(identifier):
 # 如果后端是tensorflow并且使用的是tensorflow自帶的優(yōu)化器實(shí)例，可以直接使用tensorflow原生的優(yōu)化器 
 if K.backend() == 'tensorflow':
 # Wrap TF optimizer instances
 if isinstance(identifier, tf.train.Optimizer):
  return TFOptimizer(identifier)
 # 如果以json串的形式定義optimizer并進(jìn)行參數(shù)配置
 if isinstance(identifier, dict):
 return deserialize(identifier)
 elif isinstance(identifier, six.string_types):
 # 如果以字符串形式指定optimizer，那么使用優(yōu)化器的默認(rèn)配置參數(shù)
 config = {'class_name': str(identifier), 'config': {}}
 return deserialize(config)
 if isinstance(identifier, Optimizer):
 # 如果使用keras封裝的Optimizer的實(shí)例
 return identifier
 else:
 raise ValueError('Could not interpret optimizer identifier: ' +
    str(identifier))

def get(identifier):
 if identifier is None:
 return None
 if isinstance(identifier, six.string_types):
 identifier = str(identifier)
 return deserialize(identifier)
 if isinstance(identifier, dict):
 return deserialize(identifier)
 elif callable(identifier):
 return identifier
 else:
 raise ValueError('Could not interpret '
    'loss function identifier:', identifier)

def handle_metrics(metrics, weights=None):
 metric_name_prefix = 'weighted_' if weights is not None else ''

 for metric in metrics:
 # 如果metrics是最常見(jiàn)的那種:accuracy，交叉熵
 if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
  # custom handling of accuracy/crossentropy
  # (because of class mode duality)
  output_shape = K.int_shape(self.outputs[i])
  # 如果輸出維度是1或者損失函數(shù)是二分類損失函數(shù)，那么說(shuō)明是個(gè)二分類問(wèn)題，應(yīng)該使用二分類的accuracy和二分類的的交叉熵
  if (output_shape[-1] == 1 or
  self.loss_functions[i] == losses.binary_crossentropy):
  # case: binary accuracy/crossentropy
  if metric in ('accuracy', 'acc'):
   metric_fn = metrics_module.binary_accuracy
  elif metric in ('crossentropy', 'ce'):
   metric_fn = metrics_module.binary_crossentropy
  # 如果損失函數(shù)是sparse_categorical_crossentropy,那么目標(biāo)y_input就不是one-hot的，所以就需要使用sparse的多類準(zhǔn)去率和sparse的多類交叉熵
  elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
  # case: categorical accuracy/crossentropy
  # with sparse targets
  if metric in ('accuracy', 'acc'):
   metric_fn = metrics_module.sparse_categorical_accuracy
  elif metric in ('crossentropy', 'ce'):
   metric_fn = metrics_module.sparse_categorical_crossentropy
  else:
  # case: categorical accuracy/crossentropy
  if metric in ('accuracy', 'acc'):
   metric_fn = metrics_module.categorical_accuracy
  elif metric in ('crossentropy', 'ce'):
   metric_fn = metrics_module.categorical_crossentropy
  if metric in ('accuracy', 'acc'):
   suffix = 'acc'
  elif metric in ('crossentropy', 'ce'):
   suffix = 'ce'
  weighted_metric_fn = weighted_masked_objective(metric_fn)
  metric_name = metric_name_prefix + suffix
 else:
  # 如果輸入的metric不是字符串，那么就調(diào)用metrics模塊獲取
  metric_fn = metrics_module.get(metric)
  weighted_metric_fn = weighted_masked_objective(metric_fn)
  # Get metric name as string
  if hasattr(metric_fn, 'name'):
  metric_name = metric_fn.name
  else:
  metric_name = metric_fn.__name__
  metric_name = metric_name_prefix + metric_name

 with K.name_scope(metric_name):
  metric_result = weighted_metric_fn(y_true, y_pred,
      weights=weights,
      mask=masks[i])

 # Append to self.metrics_names, self.metric_tensors,
 # self.stateful_metric_names
 if len(self.output_names) > 1:
  metric_name = self.output_names[i] + '_' + metric_name
 # Dedupe name
 j = 1
 base_metric_name = metric_name
 while metric_name in self.metrics_names:
  metric_name = base_metric_name + '_' + str(j)
  j += 1
 self.metrics_names.append(metric_name)
 self.metrics_tensors.append(metric_result)

 # Keep track of state updates created by
 # stateful metrics (i.e. metrics layers).
 if isinstance(metric_fn, Layer) and metric_fn.stateful:
  self.stateful_metric_names.append(metric_name)
  self.stateful_metric_functions.append(metric_fn)
  self.metrics_updates += metric_fn.updates

def get(identifier):
 if isinstance(identifier, dict):
 config = {'class_name': str(identifier), 'config': {}}
 return deserialize(config)
 elif isinstance(identifier, six.string_types):
 return deserialize(str(identifier))
 elif callable(identifier):
 return identifier
 else:
 raise ValueError('Could not interpret '
    'metric function identifier:', identifier)