435560056e838c96100a0e80aee9dbbe57ea4c4f,mnist/hptuning/trainer/model.py,Model,build_graph,#Model#Any#Any#Any#,85

Before Change

    if not is_training:
      tf.scalar_summary("training/hptuning/metric", accuracy_op)

    tensors.metric_updates = [loss_update, accuracy_update]
    tensors.metric_values = [loss_op, accuracy_op]
    return tensors

  def build_train_graph(self, data_paths, batch_size):

After Change

    // HYPERPARAMETER TUNING: Write the objective value.
    if not is_training:
      // TODO(b/33420312): remove the if once 0.12 is fully rolled out to prod.
      if tf.__version__ < "0.12":
        tf.scalar_summary("accuracy", accuracy_op)
        tf.scalar_summary("loss", loss_op)
        tf.scalar_summary("training/hptuning/metric", accuracy_op)
      else:
        tf.contrib.deprecated.scalar_summary("accuracy", accuracy_op)
        tf.contrib.deprecated.scalar_summary("loss", loss_op)
        tf.contrib.deprecated.scalar_summary("training/hptuning/metric",
                                             accuracy_op)

    tensors.metric_updates = loss_updates + accuracy_updates
    tensors.metric_values = [loss_op, accuracy_op]
    return tensors

Instances