def _get_dataset_next(self, files, config, batch_size):
def decode_func(value):
return [tf.string_to_number(value, out_type=tf.int32)]
dataset = dataset_builder.read_dataset(tf.data.TextLineDataset, files,
config)
dataset = dataset.map(decode_func)
dataset = dataset.batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
def _get_dataset_next(self, files, config, batch_size):
def decode_func(value):
return [tf.string_to_number(value, out_type=tf.int32)]
dataset = dataset_builder.read_dataset(
tf.data.TextLineDataset, files, config)
dataset = dataset.map(decode_func)
dataset = dataset.batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
def _get_dataset_next(self, files, config, batch_size, num_batches_skip=0):
def decode_func(value):
return [tf.string_to_number(value, out_type=tf.int32)]
dataset = dataset_builder.read_dataset(tf.data.TextLineDataset, files,
config)
dataset = dataset.map(decode_func)
dataset = dataset.batch(batch_size)
if num_batches_skip > 0:
dataset = dataset.skip(num_batches_skip)
return get_iterator_next_for_testing(dataset, self.is_tf2())
