本文整理汇总了Python中official.utils.misc.model_helpers.apply_clean方法的典型用法代码示例。如果您正苦于以下问题：Python model_helpers.apply_clean方法的具体用法？Python model_helpers.apply_clean怎么用？Python model_helpers.apply_clean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official.utils.misc.model_helpers的用法示例。
在下文中一共展示了model_helpers.apply_clean方法的5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: main
# 需要导入模块: from official.utils.misc import model_helpers [as 别名]
# 或者: from official.utils.misc.model_helpers import apply_clean [as 别名]
def main(_):
model_helpers.apply_clean(flags.FLAGS)
with logger.benchmark_context(flags.FLAGS):
return run(flags.FLAGS) 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:6，代码来源:ctl_imagenet_main.py
示例2: main
# 需要导入模块: from official.utils.misc import model_helpers [as 别名]
# 或者: from official.utils.misc.model_helpers import apply_clean [as 别名]
def main(_):
model_helpers.apply_clean(flags.FLAGS)
with logger.benchmark_context(flags.FLAGS):
stats = run(flags.FLAGS)
logging.info('Run stats:\n%s', stats) 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:7，代码来源:resnet_imagenet_main.py
示例3: main
# 需要导入模块: from official.utils.misc import model_helpers [as 别名]
# 或者: from official.utils.misc.model_helpers import apply_clean [as 别名]
def main(_):
model_helpers.apply_clean(flags.FLAGS)
stats = run(flags.FLAGS)
logging.info('Run stats:\n%s', stats) 示例4: main
# 需要导入模块: from official.utils.misc import model_helpers [as 别名]
# 或者: from official.utils.misc.model_helpers import apply_clean [as 别名]
def main(_):
model_helpers.apply_clean(FLAGS)
stats = run(flags.FLAGS)
logging.info('Run stats:\n%s', stats) 示例5: run_loop
# 需要导入模块: from official.utils.misc import model_helpers [as 别名]
# 或者: from official.utils.misc.model_helpers import apply_clean [as 别名]
def run_loop(name, train_input_fn, eval_input_fn, model_column_fn,
build_estimator_fn, flags_obj, tensors_to_log, early_stop=False):
"""Define training loop."""
model_helpers.apply_clean(flags.FLAGS)
model = build_estimator_fn(
model_dir=flags_obj.model_dir, model_type=flags_obj.model_type,
model_column_fn=model_column_fn,
inter_op=flags_obj.inter_op_parallelism_threads,
intra_op=flags_obj.intra_op_parallelism_threads)
run_params = {
'batch_size': flags_obj.batch_size,
'train_epochs': flags_obj.train_epochs,
'model_type': flags_obj.model_type,
}
benchmark_logger = logger.get_benchmark_logger()
benchmark_logger.log_run_info('wide_deep', name, run_params,
test_id=flags_obj.benchmark_test_id)
loss_prefix = LOSS_PREFIX.get(flags_obj.model_type, '')
tensors_to_log = {k: v.format(loss_prefix=loss_prefix)
for k, v in tensors_to_log.items()}
train_hooks = hooks_helper.get_train_hooks(
flags_obj.hooks, model_dir=flags_obj.model_dir,
batch_size=flags_obj.batch_size, tensors_to_log=tensors_to_log)
# Train and evaluate the model every `flags.epochs_between_evals` epochs.
for n in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
model.train(input_fn=train_input_fn, hooks=train_hooks)
results = model.evaluate(input_fn=eval_input_fn)
# Display evaluation metrics
tf.logging.info('Results at epoch %d / %d',
(n + 1) * flags_obj.epochs_between_evals,
flags_obj.train_epochs)
tf.logging.info('-' * 60)
for key in sorted(results):
tf.logging.info('%s: %s' % (key, results[key]))
benchmark_logger.log_evaluation_result(results)
if early_stop and model_helpers.past_stop_threshold(
flags_obj.stop_threshold, results['accuracy']):
break
# Export the model
if flags_obj.export_dir is not None:
export_model(model, flags_obj.model_type, flags_obj.export_dir,
model_column_fn) 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:54，代码来源:wide_deep_run_loop.py