21cd0774c8c3d41a8464427c81629075c953e7e3,models/experimental/keras/resnet50.py,,main,#Any#,59

Before Change

    // Direct evaluation with datasets is coming in TF 1.11.  For now,
    // we can perform evaluation using a standard Python generator with a smaller
    // batch size.
    batch_size = 32 * num_cores
    imagenet_eval = imagenet_input.ImageNetInput(
        is_training=False,
        data_dir=FLAGS.data,
        // In normal execution, our dataset would generate data for each TPU
        // core.  In this case, because we are feeding in from a Keras generator,
        // we want to build a single batch for all of the cores, which will then
        // be split for us.
        per_core_batch_size=batch_size)
    score = model.evaluate_generator(
        imagenet_eval.evaluation_generator(session_master),
        steps=int(APPROX_IMAGENET_TEST_IMAGES // batch_size),
        verbose=1)

After Change

    logging.info("Evaluating the model on synthetic data.")
    model.evaluate(training_images, training_labels, verbose=0)
  else:
    imagenet_train, imagenet_eval = [imagenet_input.ImageNetInput(
        is_training=is_training,
        data_dir=FLAGS.data,
        per_core_batch_size=PER_CORE_BATCH_SIZE)
                                     for is_training in [True, False]]
    logging.info("Training model using real data in directory "%s".",
                 FLAGS.data)
    num_epochs = 90  // Standard imagenet training regime.

Instances