本文整理汇总了Python中tfcode.vision_baseline_lstm.setup_to_run方法的典型用法代码示例。如果您正苦于以下问题：Python vision_baseline_lstm.setup_to_run方法的具体用法？Python vision_baseline_lstm.setup_to_run怎么用？Python vision_baseline_lstm.setup_to_run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tfcode.vision_baseline_lstm的用法示例。
在下文中一共展示了vision_baseline_lstm.setup_to_run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_args_for_config
# 需要导入模块: from tfcode import vision_baseline_lstm [as 别名]
# 或者: from tfcode.vision_baseline_lstm import setup_to_run [as 别名]
def get_args_for_config(config_name):
configs = config_name.split('.')
type = configs[0]
config_name = '.'.join(configs[1:])
if type == 'cmp':
args = config_cmp.get_args_for_config(config_name)
args.setup_to_run = cmp.setup_to_run
args.setup_train_step_kwargs = cmp.setup_train_step_kwargs
elif type == 'bl':
args = config_vision_baseline.get_args_for_config(config_name)
args.setup_to_run = vision_baseline_lstm.setup_to_run
args.setup_train_step_kwargs = vision_baseline_lstm.setup_train_step_kwargs
else:
logging.fatal('Unknown type: {:s}'.format(type))
return args 示例2: _train
# 需要导入模块: from tfcode import vision_baseline_lstm [as 别名]
# 或者: from tfcode.vision_baseline_lstm import setup_to_run [as 别名]
def _train(args):
container_name = ""
R = lambda: nav_env.get_multiplexer_class(args.navtask, args.solver.task)
m = utils.Foo()
m.tf_graph = tf.Graph()
config = tf.ConfigProto()
config.device_count['GPU'] = 1
with m.tf_graph.as_default():
with tf.device(tf.train.replica_device_setter(args.solver.ps_tasks,
merge_devices=True)):
with tf.container(container_name):
m = args.setup_to_run(m, args, is_training=True,
batch_norm_is_training=True, summary_mode='train')
train_step_kwargs = args.setup_train_step_kwargs(
m, R(), os.path.join(args.logdir, 'train'), rng_seed=args.solver.task,
is_chief=args.solver.task==0,
num_steps=args.navtask.task_params.num_steps*args.navtask.task_params.num_goals, iters=1,
train_display_interval=args.summary.display_interval,
dagger_sample_bn_false=args.arch.dagger_sample_bn_false)
delay_start = (args.solver.task*(args.solver.task+1))/2 * FLAGS.delay_start_iters
logging.error('delaying start for task %d by %d steps.',
args.solver.task, delay_start)
additional_args = {}
final_loss = slim.learning.train(
train_op=m.train_op,
logdir=args.logdir,
master=args.solver.master,
is_chief=args.solver.task == 0,
number_of_steps=args.solver.max_steps,
train_step_fn=tf_utils.train_step_custom_online_sampling,
train_step_kwargs=train_step_kwargs,
global_step=m.global_step_op,
init_op=m.init_op,
init_fn=m.init_fn,
sync_optimizer=m.sync_optimizer,
saver=m.saver_op,
startup_delay_steps=delay_start,
summary_op=None, session_config=config, **additional_args)