c1db25d9281a9374005d5c8b4e183b8e6d7d2ace,cloud_tpu/models/resnet/estimator_resnet.py,,input_fn,#Any#Any#,193

Before Change

  labels_shape = labels.get_shape().as_list()
  if labels_shape[0] is None:
    labels_shape[0] = batch_size
    labels = tf.reshape(
        labels, labels_shape, name="InputPipeline/labels/reshape")
  images = pipeline_outputs_transform(images)
  return images, labels

After Change

  dataset = dataset.batch(batch_size)
  images, labels = dataset.make_one_shot_iterator().get_next()
  // TODO(xiejw,saeta): Consider removing the sharding dimension below.
  images.set_shape(images.get_shape().merge_with(
      tf.TensorShape([batch_size, None, None, None])))
  labels.set_shape(
      labels.get_shape().merge_with(tf.TensorShape([batch_size, None])))
  images = pipeline_outputs_transform(images)
  return images, labels

Instances