def make_dataset(filename, mode): return tfrecord_batcher.tfrecord_dataset(filename, job['batch_size'], job['seq_length'], job.get('seq_depth', 4), job['num_targets'], job['target_length'], mode=mode)
def make_dataset(pat, mode): return tfrecord_batcher.tfrecord_dataset(pat, job['batch_size'], job['seq_length'], job.get('seq_depth', 4), job['target_length'], job['num_targets'], mode=mode, repeat=False)
def make_dataset(filename): return tfrecord_batcher.tfrecord_dataset( filename, job['batch_size'], job['seq_length'], job['seq_depth'], job['num_targets'], job['target_width'], shuffle=True, trim_eos=True)
def make_dataset(filename, mode): return tfrecord_batcher.tfrecord_dataset( filename, job["batch_size"], job["seq_length"], job.get("seq_depth", 4), job["target_length"], job["num_targets"], mode=mode, repeat=False, )
def make_dataset(loc, mode, is_dir=False): """ Creates the tfrecord dataset. This function is now expected to take either some filename string OR a list of filename strings as the data source for tfrecord_dataset. """ if is_dir: pattern = '' if mode == tf.estimator.ModeKeys.TRAIN: pattern = 'train-*.tfr' elif mode == tf.estimator.ModeKeys.EVAL: pattern = 'valid-*.tfr' elif mode == tf.estimator.ModeKeys.PREDICT: pattern = 'test-*.tfr' else: raise Exception('unrecognized tfrecord mode. Aborting.') pattern = path.join(loc, pattern) return tfrecord_batcher.tfrecord_dataset(pattern, job['batch_size'], job['seq_length'], job['seq_depth'], job['target_length'], job['num_targets'], mode=mode, repeat=False) else: return tfrecord_batcher.tfrecord_dataset(loc, job['batch_size'], job['seq_length'], job.get('seq_depth', 4), job['target_length'], job['num_targets'], mode=mode, repeat=False)