bert_max_doc_str_max_str_pos_clean_doc_noise_doc/run_doc_qa.py
def start_epoch(self, batch_size=None):
        """Prepares for a new epoch."""
        tf.logging.info("The data reader has processed {0} features".format(
            self.processed_sample_cnt))

        tf.logging.info("Starts a new epoch!")

        # This might be too costly.
        # if self.shuffle_data:
        #     tf.logging.info("Shuffles the file globally.")
        #     with tf.gfile.Open(self.feature_filename, mode='r') as fin:
        #         lines = [line for line in fin]

        #     self.rng.shuffle(lines)
        #     with tf.gfile.GFile(self.feature_filename, mode='w') as fout:
        #         for line in lines:
        #             fout.write(line)

        #     del lines

        #     tf.logging.info(
        #         "Shuffle data of chunk_size {0}".format(self.chunk_size))

        self.processed_sample_cnt = 0

        tf.logging.info(
            'The new epoch will have batch_size: {0}'.format(self.batch_size))
Prepares for a new epoch.
Prepares for a new epoch.
Prepares for a new epoch .
def start_epoch(self, batch_size=None):
        tf.logging.info("The data reader has processed {0} features".format(
            self.processed_sample_cnt))
        tf.logging.info("Starts a new epoch!")
        self.processed_sample_cnt = 0
        tf.logging.info(
            'The new epoch will have batch_size: {0}'.format(self.batch_size))
def start_epoch ( self , batch_size = None ) : tf . logging . info ( "The data reader has processed {0} features" . format ( self . processed_sample_cnt ) ) tf . logging . info ( "Starts a new epoch!" ) self . processed_sample_cnt = 0 tf . logging . info ( 'The new epoch will have batch_size: {0}' . format ( self . batch_size ) )
Prepares for a new epoch.
Prepares for a new epoch .
"""Prepares for a new epoch.""" # This might be too costly. # if self.shuffle_data: #     tf.logging.info("Shuffles the file globally.") #     with tf.gfile.Open(self.feature_filename, mode='r') as fin: #         lines = [line for line in fin] #     self.rng.shuffle(lines) #     with tf.gfile.GFile(self.feature_filename, mode='w') as fout: #         for line in lines: #             fout.write(line) #     del lines #     tf.logging.info( #         "Shuffle data of chunk_size {0}".format(self.chunk_size))
self
batch_size