Interface should be public for external usage (#522)

* Interface should be public for external usage * Fix #523 * Fix google format

Author: nfeybesse

tensorflow-framework/src/main/java/org/tensorflow/framework/losses/impl/LossesHelper.java

@@ -51,7 +51,7 @@ public class LossesHelper {
  * @param tf the TensorFlow Ops
  * @param predictions Predicted values, a <code>Operand</code> of arbitrary dimensions.
  * @param labels Optional label <code>Operand</code> whose dimensions match <code>prediction
- * </code>.
+ * </code> .
  * @param <T> the data type for the labels, predictions and result
  * @return LossTuple of <code>prediction</code>, <code>label</code>,<code>sampleWeight</code> will
  * be null. Each of them possibly has the last dimension squeezed, <code>sampleWeight</code>
@@ -77,7 +77,7 @@ public static <T extends TNumber> LossTuple<T> squeezeOrExpandDimensions(
  * @param tf the TensorFlow Ops
  * @param predictions Predicted values, a <code>Operand</code> of arbitrary dimensions.
  * @param labels Optional label <code>Operand</code> whose dimensions match <code>prediction
- * </code>.
+ * </code> .
  * @param sampleWeights Optional sample weight(s) <code>Operand</code> whose dimensions match
  * <code>
  * prediction</code>.
@@ -179,7 +179,7 @@ private static <T extends TNumber> Operand<T> maybeExpandWeights(
  *
  * @param tf the TensorFlowOps
  * @param labels Label values, a <code>Tensor</code> whose dimensions match <code>predictions
- * </code>.
+ * </code> .
  * @param predictions Predicted values, a <code>Tensor</code> of arbitrary dimensions.
  * @param <T> the data type for the labels, predictions and result
  * @return <code>labels</code> and <code>predictions</code>, possibly with last dim squeezed.
@@ -194,7 +194,7 @@ public static <T extends TNumber> LossTuple<T> removeSqueezableDimensions(
  *
  * @param tf the TensorFlowOps
  * @param labels Label values, a <code>Operand</code> whose dimensions match <code>predictions
- * </code>.
+ * </code> .
  * @param predictions Predicted values, a <code>Tensor</code> of arbitrary dimensions.
  * @param expectedRankDiff Expected result of <code>rank(predictions) - rank(labels)</code>.
  * @param <T> the data type for the labels, predictions and result
@@ -222,11 +222,13 @@ public static <T extends TNumber> LossTuple<T> removeSqueezableDimensions(
  // Use dynamic rank.
 
  // TODO: hold for lazy select feature,
- // Operand<TInt32> rankDiff = tf.math.sub(tf.rank(predictions), tf.rank(labels));
+ // Operand<TInt32> rankDiff = tf.math.sub(tf.rank(predictions),
+ // tf.rank(labels));
  if (predictionsRank == Shape.UNKNOWN_SIZE && Shape.isCompatible(predictionsShape.size(-1), 1)) {
  /*
- * TODO, if we ever get a select that does lazy evaluation, but for now do the tf.squeeze
- * predictions = tf.select( tf.math.equal(tf.constant(expectedRankDiff+1),rankDiff ),
+ * TODO, if we ever get a select that does lazy evaluation, but for now do the
+ * tf.squeeze predictions = tf.select(
+ * tf.math.equal(tf.constant(expectedRankDiff+1),rankDiff ),
  * tf.squeeze(predictions, Squeeze.axis(Arrays.asList(-1L))), predictions ); *
  */
  predictions = tf.squeeze(predictions, Squeeze.axis(Collections.singletonList(-1L)));
@@ -282,11 +284,12 @@ private static <T extends TNumber> Operand<T> reduceWeightedLoss(
  if (reduction == Reduction.NONE) {
  loss = weightedLoss;
  } else {
- loss =
- tf.reduceSum(weightedLoss, allAxes(tf, weightedLoss), ReduceSum.keepDims(Boolean.FALSE));
  if (reduction == Reduction.AUTO || reduction == Reduction.SUM_OVER_BATCH_SIZE) {
- loss = safeMean(tf, loss, weightedLoss.shape().size());
- }
+ loss = safeMean(tf, weightedLoss);
+ } else
+ loss =
+ tf.reduceSum(
+ weightedLoss, allAxes(tf, weightedLoss), ReduceSum.keepDims(Boolean.FALSE));
  }
  return loss;
  }
@@ -301,10 +304,10 @@ private static <T extends TNumber> Operand<T> reduceWeightedLoss(
  * @return A scalar representing the mean of <code>losses</code>. If <code>numElements</code> is
  * zero, then zero is returned.
  */
- public static <T extends TNumber> Operand<T> safeMean(
-  Ops tf, Operand<T> losses, long numElements) {
- Operand<T> totalLoss = tf.reduceSum(losses, allAxes(tf, losses));
- return tf.math.divNoNan(totalLoss, cast(tf, tf.constant(numElements), losses.type()));
+ public static <T extends TNumber> Operand<T> safeMean(Ops tf, Operand<T> losses) {
+ Operand<T> totalLoss =
+  tf.reduceSum(losses, allAxes(tf, losses), ReduceSum.keepDims(Boolean.FALSE));
+ return tf.math.divNoNan(totalLoss, cast(tf, tf.shape.size(tf.shape(losses)), losses.type()));
  }
 
  /**
@@ -348,7 +351,8 @@ public static <T extends TNumber> Operand<T> rangeCheck(
  tf.math.logicalAnd(
  tf.reduceAll(tf.math.greaterEqual(values, minValue), allDims),
  tf.reduceAll(tf.math.lessEqual(values, maxValue), allDims));
- // Graph and Eager mode need to be handled differently, control dependencies are not allowed in
+ // Graph and Eager mode need to be handled differently, control dependencies are
+ // not allowed in
  // Eager mode
  if (tf.scope().env().isGraph()) {
  AssertThat assertThat =
@@ -398,7 +402,8 @@ public static <T extends TNumber> Operand<T> valueCheck(
  } else return values;
  } else { // use dynamic shape
  Operand<TBool> cond = tf.math.equal(tf.shape.size(tf.shape(diff.out())), tf.constant(0));
- // Graph and Eager mode need to be handled differently, control dependencies are not allowed
+ // Graph and Eager mode need to be handled differently, control dependencies are
+ // not allowed
  // in Eager mode
  if (tf.scope().env().isGraph()) {
  AssertThat assertThat =

tensorflow-framework/src/main/java/org/tensorflow/framework/metrics/Metric.java

@@ -22,7 +22,7 @@
 import org.tensorflow.types.family.TNumber;
 
 /** Interface for metrics */
-interface Metric {
+public interface Metric {
 
  /**
  * Creates a List of Operations to update the metric state based on input values.