def __init__(self,
optimizer: str = 'AdamOptimizer',
clip_norm: float = None,
momentum: float = None,
**kwargs) -> None:
TFModel.__init__(self, **kwargs)
try:
self._optimizer = cls_from_str(optimizer)
except Exception:
self._optimizer = getattr(tf.train, optimizer.split(':')[-1])
if not issubclass(self._optimizer, tf.train.Optimizer):
raise ConfigError("`optimizer` should be tensorflow.train.Optimizer subclass")
self._clip_norm = clip_norm
if (momentum is None) and\
self._optimizer not in (tf.train.AdagradOptimizer,
tf.train.AdagradOptimizer,
tf.train.GradientDescentOptimizer,
tf.train.ProximalGradientDescentOptimizer,
tf.train.ProximalAdagradOptimizer):
momentum = 0.9
kwargs['momentum'] = momentum
LRScheduledModel.__init__(self, **kwargs)
def __init__(self, **kwargs):
"""
Initialize model with given parameters
Args:
**kwargs: dictionary of parameters
"""
self.opt = kwargs
KerasModel.__init__(self, **kwargs)
if not (isinstance(kwargs.get("learning_rate"), float)
and isinstance(kwargs.get("learning_rate_decay"), float)):
LRScheduledModel.__init__(self, **kwargs)
def __init__(self,
optimizer: str = 'AdamOptimizer',
clip_norm: float = None,
momentum: float = None,
**kwargs) -> None:
TFModel.__init__(self, **kwargs)
try:
self._optimizer = cls_from_str(optimizer)
except Exception:
self._optimizer = getattr(tf.train, optimizer.split(':')[-1])
if not issubclass(self._optimizer, tf.train.Optimizer):
raise ConfigError("`optimizer` should be tensorflow.train.Optimizer subclass")
self._clip_norm = clip_norm
LRScheduledModel.__init__(self, momentum=momentum, **kwargs)
def process_event(self, *args, **kwargs):
LRScheduledModel.process_event(self, *args, **kwargs)