本文整理汇总了Python中keras.callbacks.CallbackList方法的典型用法代码示例。如果您正苦于以下问题：Python callbacks.CallbackList方法的具体用法？Python callbacks.CallbackList怎么用？Python callbacks.CallbackList使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.callbacks的用法示例。
在下文中一共展示了callbacks.CallbackList方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _get_callbacks
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import CallbackList [as 别名]
def _get_callbacks(self):
"""
Returns a set of Callbacks which are used to perform various functions within Keras' .fit method.
Here, we use an early stopping callback to add patience with respect to the validation metric and
a Lambda callback which performs the model specific callbacks which you might want to build into
a model, such as re-encoding some background knowledge.
Additionally, there is also functionality to create Tensorboard log files. These can be visualised
using 'tensorboard --logdir /path/to/log/files' after training.
"""
early_stop = EarlyStopping(monitor=self.validation_metric, patience=self.patience)
model_callbacks = LambdaCallback(on_epoch_begin=lambda epoch, logs: self._pre_epoch_hook(epoch),
on_epoch_end=lambda epoch, logs: self._post_epoch_hook(epoch))
callbacks = [early_stop, model_callbacks]
if self.debug_params:
debug_callback = LambdaCallback(on_epoch_end=lambda epoch, logs:
self.__debug(self.debug_params["layer_names"],
self.debug_params.get("masks", []), epoch))
callbacks.append(debug_callback)
return CallbackList(callbacks)
# Some witchcraft is happening here - we don't specify the epoch replacement variable
# checkpointing string, because Keras does that within the callback if we specify it here.
if self.save_models:
checkpointing = ModelCheckpoint(self.model_prefix + "_weights_epoch={epoch:d}.h5",
save_best_only=True, save_weights_only=True,
monitor=self.validation_metric)
callbacks.append(checkpointing)
return CallbackList(callbacks) 示例2: _prepare_callbacks
# 需要导入模块: from keras import callbacks [as 别名]
# 或者: from keras.callbacks import CallbackList [as 别名]
def _prepare_callbacks(self,
callbacks: List[Callback],
val_ins: List[numpy.array],
epochs: int,
batch_size: int,
num_train_samples: int,
callback_metrics: List[str],
do_validation: bool,
verbose: int):
"""
Sets up Keras callbacks to perform various monitoring functions during training.
"""
self.history = History() # pylint: disable=attribute-defined-outside-init
callbacks = [BaseLogger()] + (callbacks or []) + [self.history]
if verbose:
callbacks += [ProgbarLogger()]
callbacks = CallbackList(callbacks)
# it's possible to callback a different model than self
# (used by Sequential models).
if hasattr(self, 'callback_model') and self.callback_model:
callback_model = self.callback_model
else:
callback_model = self # pylint: disable=redefined-variable-type
callbacks.set_model(callback_model)
callbacks.set_params({
'batch_size': batch_size,
'epochs': epochs,
'samples': num_train_samples,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics or [],
})
callbacks.on_train_begin()
callback_model.stop_training = False
for cbk in callbacks:
cbk.validation_data = val_ins
return callbacks, callback_model