(num_clips)
Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
    default.

    @param num_clips: The number of clips to process. Default = 5000000 (set in __main__).

    @warning: This can take a couple of hours to complete with large numbers of clips.
Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
    default.
Processes random training clips from the full training data . Saves to TRAIN_DIR_CLIPS by default .
def process_training_data(num_clips):
    """
    Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
    default.

    @param num_clips: The number of clips to process. Default = 5000000 (set in __main__).

    @warning: This can take a couple of hours to complete with large numbers of clips.
    """
    num_prev_clips = len(glob(c.TRAIN_DIR_CLIPS + '*'))

    for clip_num in xrange(num_prev_clips, num_clips + num_prev_clips):
        clip = process_clip()

        np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)

        if (clip_num + 1) % 100 == 0: print 'Processed %d clips' % (clip_num + 1)
def process_training_data ( num_clips ) : num_prev_clips = len ( glob ( c . TRAIN_DIR_CLIPS + '*' ) ) for clip_num in xrange ( num_prev_clips , num_clips + num_prev_clips ) : clip = process_clip ( ) np . savez_compressed ( c . TRAIN_DIR_CLIPS + str ( clip_num ) , clip ) if ( clip_num + 1 ) % 100 == 0 : print 'Processed %d clips' % ( clip_num + 1 )