Learning rate

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Optimizer.java

@@ -111,20 +111,47 @@ protected Optimizer(Graph graph, String name, float learningRate) {
     setLearningRate(learningRate);
   }
 
+  /**
+   * Creates a name by combining a variable name and a slot name
+   *
+   * @param variable the variable
+   * @param slotName the name of the slot
+   * @return the combined name
+   */
   public static String createName(Output<? extends TType> variable, String slotName) {
     return variable.op().name() + "-" + slotName;
   }
 
+  /**
+   * Minimizes the loss by updating the variables
+   *
+   * @param loss the loss operation that returns the value to minimize
+   * @return returns op that minimizes the loss by updating the listed variables
+   */
   public Op minimize(Operand<?> loss) {
     return minimize(loss, getOptimizerName() + "-minimize");
   }
 
+  /**
+   * Minimizes the loss by updating the variables
+   *
+   * @param loss the loss operation that returns the value to minimize
+   * @param name the name for the minimize operation
+   * @return op that minimizes the loss by updating the listed variables
+   */
   public Op minimize(Operand<?> loss, String name) {
     List<GradAndVar<?>> gradsAndVars = computeGradients(loss);
 
     return applyGradients(gradsAndVars, name);
   }
 
+  /**
+   * Computes the gradients based on a loss operand.
+   *
+   * @param loss the loss operation
+   * @param <T> the data type of the loss, gradients and variables.
+   * @return the computed gradients
+   */
   public <T extends TType> List<GradAndVar<?>> computeGradients(Operand<?> loss) {
     List<Operation> variables = new ArrayList<>();
     graph
@@ -156,6 +183,13 @@ public <T extends TType> List<GradAndVar<?>> computeGradients(Operand<?> loss) {
     return gradVarPairs;
   }
 
+  /**
+   * Applies gradients to variables
+   *
+   * @param gradsAndVars the list of (gradient, variable) pairs.
+   * @param name the name of the apply gradients operation
+   * @return an Op that applies the gradients to the variables.
+   */
   public Op applyGradients(List<GradAndVar<? extends TType>> gradsAndVars, String name) {
     List<Output<? extends TType>> variables =
         gradsAndVars.stream().map(GradAndVar::getVariable).collect(Collectors.toList());
@@ -242,6 +276,13 @@ protected Optional<Op> prepare(String scopeName) {
    */
   protected void createSlots(List<Output<? extends TType>> variables) {}
 
+  /**
+   * Generates the gradient update operations for the specific variable and gradient.
+   *
+   * @param gradVarPair the list of (gradient, variable) pairs.
+   * @param <T> the datatype of the gradients and variables.
+   * @return An operand which applies the desired optimizer update to the variable.
+   */
   private <T extends TType> Op applyDense(GradAndVar<T> gradVarPair) {
     return applyDense(gradVarPair.getGradient(), gradVarPair.getVariable());
   }
@@ -280,20 +321,20 @@ protected Op finish(List<Op> updateOperations, String name) {
   /**
    * Sets the learning rate
    *
-   * @param learningRate the learning rate
+   * @param newLearningRate the new earning rate
    */
-  public final void setLearningRate(float learningRate) {
-    if (this.learningRatePlaceholder == null) {
-      this.learningRatePlaceholder =
+  public final void setLearningRate(float newLearningRate) {
+    if (learningRatePlaceholder == null) {
+      learningRatePlaceholder =
           tf.withSubScope(LEARNING_RATE)
               .placeholder(TFloat32.DTYPE, Placeholder.shape(Shape.scalar()));
     }
 
-    if (this.learningRate != learningRate) {
-      if (this.learningRateTensor != null) this.learningRateTensor.close();
-      this.learningRate = learningRate;
-      this.learningRateTensor = TFloat32.scalarOf(this.learningRate);
-      this.feedMap = Collections.singletonMap(this.learningRatePlaceholder, learningRateTensor);
+    if (learningRate != newLearningRate) {
+      if (learningRateTensor != null) learningRateTensor.close();
+      learningRate = newLearningRate;
+      learningRateTensor = TFloat32.scalarOf(learningRate);
+      feedMap = Collections.singletonMap(learningRatePlaceholder, learningRateTensor);
     }
   }
 
@@ -303,7 +344,7 @@ public final void setLearningRate(float learningRate) {
    * @return the learning rate
    */
   public float getLearningRate() {
-    return this.learningRate;
+    return learningRate;
   }
 
   /**
@@ -312,7 +353,7 @@ public float getLearningRate() {
    * @return the learning rate Operand
    */
   protected Operand<TFloat32> getLearningRateOperand() {
-    return this.learningRatePlaceholder;
+    return learningRatePlaceholder;
   }
 
   /**
@@ -323,13 +364,15 @@ protected Operand<TFloat32> getLearningRateOperand() {
    *     Operand has been set.
    */
   public Map<Operand<? extends TType>, Tensor<? extends TType>> getFeedMap() {
-    return this.feedMap;
+    return feedMap;
   }
 
+  /** {@inheritDoc}  */
   public void close() {
     // close the learningRate Tensor if it exists.
-    if (this.feedMap != null) {
-      this.feedMap.get(this.learningRatePlaceholder).close();
+    if (learningRateTensor != null) {
+      learningRateTensor.close();
+      learningRateTensor = null;
     }
   }