pr_curve_streaming_op creates duplicate graphs with offset of one step

[alienczf]

I believe submitting both the stats tensors and op to tf.summary.tensor_summary creates duplicate plots

    def compute_summary(tp, fp, tn, fn, collections):
      precision = tp / tf.maximum(_MINIMUM_COUNT, tp + fp)
      recall = tp / tf.maximum(_MINIMUM_COUNT, tp + fn)

      return _create_tensor_summary(
          name,
          tp,
          fp,
          tn,
          fn,
          precision,
          recall,
          num_thresholds,
          display_name,
          description,
          collections)

    pr_curve = compute_summary(tp, fp, tn, fn, metrics_collections)
    update_op = compute_summary(update_tp, update_fp, update_tn, update_fn,
                                updates_collections)

    return pr_curve, update_op

I have made a quick fix that looks like this:

    def compute_summary(tp, fp, tn, fn, collections):
      precision = tp / tf.maximum(_MINIMUM_COUNT, tp + fp)
      recall = tp / tf.maximum(_MINIMUM_COUNT, tp + fn)

      return tf.stack([
        tf.cast(tp, tf.float32),
        tf.cast(fp, tf.float32),
        tf.cast(tn, tf.float32),
        tf.cast(fn, tf.float32),
        tf.cast(precision, tf.float32),
        tf.cast(recall, tf.float32)
      ])

    pr_curve = tf.summary.tensor_summary(
      name='pr_curves',
      tensor=compute_summary(tp, fp, tn, fn, metrics_collections),
      collections=metrics_collections,
      summary_metadata=metadata.create_summary_metadata(
        display_name=display_name if display_name is not None else name,
        description=description or '',
        num_thresholds=num_thresholds
      )
    )

    update_op = compute_summary(update_tp, update_fp, update_tn, update_fn,
                                updates_collections)

    return pr_curve, update_op

This removes the duplicate plot but most of the code are a bad code duplication.

Will appreciate any recommendations for fix without code change or maybe a cleaner fix to be committed for pr curves