DatasetIterator

מייצג את המצב של איטרציה באמצעות Datset tf.data. DatasetIterator אינו java.util.Iterator. במצב להוט, 'מערך נתונים' יכול לשמש כ-Iterable, ומחזיר רכיבי מערך נתונים בכל איטרציה.

דוגמה: איטרציה במצב גרף.

// Create input tensors
 Operand<?> features = tf.constant( ... );
 Operand<?> labels = tf.constant( ... );


 Dataset dataset = Dataset
         .fromTensorSlices(XTensor, yTensor);
         .batch(BATCH_SIZE);

 DatasetIterator iterator = dataset.makeInitializeableIterator();
 List<Operand<?>> components = iterator.getNext();
 Operand<?> featureBatch = components.get(0);
 Operand<?> labelBatch = components.get(1);

 // Build a TensorFlow graph that does something on each element.
 loss = computeModelLoss(featureBatch, labelBatch);

 optimizer = ... // create an optimizer
 trainOp = optimizer.minimize(loss);

 try (Session session = new Session(graph) {
   while (true) {
     session.run(iterator.getInitializer());
     try {
         session
           .addTarget(trainOp)
           .fetch(loss)
           .run();

         ...
      catch (TFOutOfRangeError e) {
         System.out.println("finished iterating.");
         break;
     }
   }
 }

 }

דוגמה: איטרציה במצב להוט.

// Create input tensors
 Operand<?> features = tf.constant( ... );
 Operand<?> labels = tf.constant( ... );

 int BATCH_SIZE = ...

 Dataset dataset = Dataset
         .fromTensorSlices(features, labels)
         .batch(BATCH_SIZE);
 DatasetIterator iterator = dataset.makeIterator();

 Optimizer optimizer = ... // create an optimizer

 for (List<Operand<?>> components : dataset) {
     Operand<?> featureBatch = components.get(0);
     Operand<?> labelBatch = components.get(1);

     loss = computeModelLoss(featureBatch, labelBatch);
     trainOp = optimizer.minimize(loss);
 
 }