本文整理汇总了Python中tensorflow.contrib.tpu.TPUConfig方法的典型用法代码示例。如果您正苦于以下问题：Python tpu.TPUConfig方法的具体用法？Python tpu.TPUConfig怎么用？Python tpu.TPUConfig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.contrib.tpu的用法示例。
在下文中一共展示了tpu.TPUConfig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: make_tpu_run_config
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import TPUConfig [as 别名]
def make_tpu_run_config(master, seed, model_dir, iterations_per_loop,
save_checkpoints_steps):
return contrib_tpu.RunConfig(
master=master,
evaluation_master=master,
model_dir=model_dir,
save_checkpoints_steps=save_checkpoints_steps,
cluster=None,
tf_random_seed=seed,
tpu_config=contrib_tpu.TPUConfig(iterations_per_loop=iterations_per_loop)) 示例2: main
# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import TPUConfig [as 别名]
def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tpu_cluster_resolver = (
contrib_cluster_resolver.TPUClusterResolver(
tpu=[FLAGS.tpu_name], zone=FLAGS.tpu_zone, project=FLAGS.gcp_project))
tpu_grpc_url = tpu_cluster_resolver.get_master()
config = contrib_tpu.RunConfig(
master=tpu_grpc_url,
evaluation_master=tpu_grpc_url,
model_dir=FLAGS.model_dir,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_shards))
kwargs = {}
if FLAGS.train_batch_size:
kwargs['batch_size'] = FLAGS.train_batch_size
train_and_eval_dict = model_lib.create_estimator_and_inputs(
run_config=config,
hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
pipeline_config_path=FLAGS.pipeline_config_path,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
use_tpu_estimator=True,
use_tpu=FLAGS.use_tpu,
num_shards=FLAGS.num_shards,
save_final_config=FLAGS.mode == 'train',
**kwargs)
estimator = train_and_eval_dict['estimator']
train_input_fn = train_and_eval_dict['train_input_fn']
eval_input_fns = train_and_eval_dict['eval_input_fns']
eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
train_steps = train_and_eval_dict['train_steps']
if FLAGS.mode == 'train':
estimator.train(input_fn=train_input_fn, max_steps=train_steps)
# Continuously evaluating.
if FLAGS.mode == 'eval':
if FLAGS.eval_training_data:
name = 'training_data'
input_fn = eval_on_train_input_fn
else:
name = 'validation_data'
# Currently only a single eval input is allowed.
input_fn = eval_input_fns[0]
model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, train_steps,
name, FLAGS.max_eval_retries)