9de6656a779e73ac61995bd87044af21b3f37951,models/experimental/amoeba_net/amoeba_net_model.py,InputPipeline,input_fn,#InputPipeline#Any#,454

Before Change

          self._dataset_parser,
          num_parallel_calls=64)

      dataset = dataset.prefetch(batch_size)

      dataset = dataset.apply(
          tf.contrib.data.batch_and_drop_remainder(batch_size))

      dataset = dataset.prefetch(2)  // Prefetch overlaps in-feed with training
      images, labels = dataset.make_one_shot_iterator().get_next()
    else:
      images = tf.random_uniform(
          [batch_size, self.hparams.image_size,
           self.hparams.image_size, 3], minval=-1, maxval=1)

After Change

      dataset = dataset.map(set_shapes)

    dataset = dataset.prefetch(32)  // Prefetch overlaps in-feed with training
    return dataset  // Must return the dataset and not tensors for high perf!


class LoadEMAHook(tf.train.SessionRunHook):

Instances