本文整理匯總了Python中keras.callbacks.ProgbarLogger方法的典型用法代碼示例。如果您正苦於以下問題：Python callbacks.ProgbarLogger方法的具體用法？Python callbacks.ProgbarLogger怎麽用？Python callbacks.ProgbarLogger使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.callbacks的用法示例。
在下文中一共展示了callbacks.ProgbarLogger方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: from keras import callbacks [as 別名]
# 或者: from keras.callbacks import ProgbarLogger [as 別名]
def __init__(self, show_metrics=None):
super(ProgbarLogger, self).__init__()
self.show_metrics = show_metrics 示例2: on_train_begin
# 需要導入模塊: from keras import callbacks [as 別名]
# 或者: from keras.callbacks import ProgbarLogger [as 別名]
def on_train_begin(self, logs=None):
super(ProgbarLogger, self).on_train_begin(logs)
if self.show_metrics:
self.params['metrics'] = self.show_metrics 示例3: train
# 需要導入模塊: from keras import callbacks [as 別名]
# 或者: from keras.callbacks import ProgbarLogger [as 別名]
def train(self, epochs, steps_per_epoch, initial_epoch=0,
end_of_epoch_callback=None, verbose=1):
epoch = initial_epoch
logger = ProgbarLogger(count_mode='steps')
logger.set_params({
'epochs': epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'metrics': self.metric_names})
logger.on_train_begin()
while epoch < epochs:
step = 0
batch = 0
logger.on_epoch_begin(epoch)
while step < steps_per_epoch:
self.batch_logs['batch'] = batch
logger.on_batch_begin(batch, self.batch_logs)
for i in range(len(self.models)):
x, y = next(self.output_generators[i])
outs = self.models[i].train_on_batch(x, y)
if not isinstance(outs, list):
outs = [outs]
if self.print_full_losses:
for l, o in zip(self.metric_names, outs):
self.batch_logs[l] = o
else:
self.batch_logs[self.metric_names[i]] = outs[0]
logger.on_batch_end(batch, self.batch_logs)
step += 1
batch += 1
logger.on_epoch_end(epoch)
if end_of_epoch_callback is not None:
end_of_epoch_callback(epoch)
epoch += 1 示例4: _prepare_callbacks
# 需要導入模塊: from keras import callbacks [as 別名]
# 或者: from keras.callbacks import ProgbarLogger [as 別名]
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