8e57685f2815b20bb27e88cad056f61939b7e86e,census/tensorflowcore/trainer/model.py,,input_fn,#Any#Any#Any#Any#Any#,266

Before Change

      A (features, indices) tuple where features is a dictionary of
        Tensors, and indices is a single Tensor of label indices.
  
  filename_queue = tf.train.string_input_producer(
      filenames, num_epochs=num_epochs, shuffle=shuffle)
  reader = tf.TextLineReader(skip_header_lines=skip_header_lines)

  _, rows = reader.read_up_to(filename_queue, num_records=batch_size)

  // Parse the CSV File
  features = parse_csv(rows)

  // This operation builds up a buffer of parsed tensors, so that parsing
  // input data doesn"t block training
  // If requested it will also shuffle
  if shuffle:
    features = tf.train.shuffle_batch(
        features,
        batch_size,
        min_after_dequeue=2 * batch_size + 1,
        capacity=batch_size * 10,
        num_threads=multiprocessing.cpu_count(),
        enqueue_many=True,
        allow_smaller_final_batch=True
    )
  else:
    features = tf.train.batch(
        features,
        batch_size,
        capacity=batch_size * 10,
        num_threads=multiprocessing.cpu_count(),
        enqueue_many=True,
        allow_smaller_final_batch=True
    )
  return features, features.pop(LABEL_COLUMN)

After Change

        Tensors, and indices is a single Tensor of label indices.
  

  dataset = tf.data.TextLineDataset(filenames).skip(skip_header_lines).map(parse_csv)

  if shuffle:
    dataset = dataset.shuffle(buffer_size=batch_size * 10)
  dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)
  iterator = dataset.make_one_shot_iterator()
  features = iterator.get_next()
  return features, features.pop(LABEL_COLUMN)

Instances