本文整理汇总了Python中tensorflow.LegacySyncReplicasOptimizer方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.LegacySyncReplicasOptimizer方法的具体用法？Python tensorflow.LegacySyncReplicasOptimizer怎么用？Python tensorflow.LegacySyncReplicasOptimizer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tensorflow的用法示例。
在下文中一共展示了tensorflow.LegacySyncReplicasOptimizer方法的1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: train
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import LegacySyncReplicasOptimizer [as 别名]
def train(loss, init_fn, hparams):
"""Wraps slim.learning.train to run a training loop.
Args:
loss: a loss tensor
init_fn: A callable to be executed after all other initialization is done.
hparams: a model hyper parameters
"""
optimizer = create_optimizer(hparams)
if FLAGS.sync_replicas:
replica_id = tf.constant(FLAGS.task, tf.int32, shape=())
optimizer = tf.LegacySyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
replica_id=replica_id,
total_num_replicas=FLAGS.total_num_replicas)
sync_optimizer = optimizer
startup_delay_steps = 0
else:
startup_delay_steps = 0
sync_optimizer = None
train_op = slim.learning.create_train_op(
loss,
optimizer,
summarize_gradients=True,
clip_gradient_norm=FLAGS.clip_gradient_norm)
slim.learning.train(
train_op=train_op,
logdir=FLAGS.train_log_dir,
graph=loss.graph,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
number_of_steps=FLAGS.max_number_of_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
startup_delay_steps=startup_delay_steps,
sync_optimizer=sync_optimizer,
init_fn=init_fn)