本文整理匯總了Python中tensor2tensor.data_generators.generator_utils.dev_data_filenames方法的典型用法代碼示例。如果您正苦於以下問題：Python generator_utils.dev_data_filenames方法的具體用法？Python generator_utils.dev_data_filenames怎麽用？Python generator_utils.dev_data_filenames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tensor2tensor.data_generators.generator_utils的用法示例。
在下文中一共展示了generator_utils.dev_data_filenames方法的3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: generate_data_for_problem
# 需要導入模塊: from tensor2tensor.data_generators import generator_utils [as 別名]
# 或者: from tensor2tensor.data_generators.generator_utils import dev_data_filenames [as 別名]
def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = generator_utils.train_data_filenames(
problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir, num_shards)
generator_utils.generate_files(training_gen(), train_output_files,
FLAGS.max_cases)
tf.logging.info("Generating development data for %s.", problem)
dev_output_files = generator_utils.dev_data_filenames(
problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir, 1)
generator_utils.generate_files(dev_gen(), dev_output_files)
all_output_files = train_output_files + dev_output_files
generator_utils.shuffle_dataset(all_output_files) 示例2: dev_filepaths
# 需要導入模塊: from tensor2tensor.data_generators import generator_utils [as 別名]
# 或者: from tensor2tensor.data_generators.generator_utils import dev_data_filenames [as 別名]
def dev_filepaths(self, data_dir, num_shards, shuffled):
file_basename = self.dataset_filename()
if not shuffled:
file_basename += generator_utils.UNSHUFFLED_SUFFIX
return generator_utils.dev_data_filenames(file_basename, data_dir,
num_shards) 示例3: generate_data_for_problem
# 需要導入模塊: from tensor2tensor.data_generators import generator_utils [as 別名]
# 或者: from tensor2tensor.data_generators.generator_utils import dev_data_filenames [as 別名]
def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = generator_utils.train_data_filenames(
problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir,
num_train_shards)
generator_utils.generate_files(training_gen(), train_output_files,
FLAGS.max_cases)
num_dev_shards = int(num_train_shards * 0.1)
tf.logging.info("Generating development data for %s.", problem)
dev_output_files = generator_utils.dev_data_filenames(
problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir,
num_dev_shards)
generator_utils.generate_files(dev_gen(), dev_output_files)
num_test_shards = int(num_train_shards * 0.1)
test_output_files = []
test_gen_data = test_gen()
if test_gen_data is not None:
tf.logging.info("Generating test data for %s.", problem)
test_output_files = generator_utils.test_data_filenames(
problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir,
num_test_shards)
generator_utils.generate_files(test_gen_data, test_output_files)
all_output_files = train_output_files + dev_output_files + test_output_files
generator_utils.shuffle_dataset(all_output_files)