cbef674a4e8553cd3817b94b707a63d2214c7533,models/experimental/resnet50_keras/resnet50.py,,main,#Any#,151

Before Change

                   "generator.")
      // We feed the inputs from a Python generator, so we need to build a single
      // batch for all of the cores, which will be split on TPU.
      imagenet_eval = imagenet_input.ImageNetInput(
          is_training=False,
          data_dir=FLAGS.data,
          per_core_batch_size=BATCH_SIZE)
      score = eval_utils.multi_top_k_accuracy(
          model, imagenet_eval.evaluation_generator(K.get_session()),
          EVAL_STEPS)
    else:
      imagenet_eval = imagenet_input.ImageNetInput(
          is_training=False,
          data_dir=FLAGS.data,

After Change

    // If evaluating top 5 accuracy, we feed the inputs from a Python generator,
    // so we need to build a single batch for all of the cores, which will be
    // split on TPU.
    per_core_batch_size = (
        BATCH_SIZE if FLAGS.eval_top_5_accuracy else PER_CORE_BATCH_SIZE)
    imagenet_validation = imagenet_input.ImageNetInput(
        is_training=False,
        data_dir=FLAGS.data,
        per_core_batch_size=per_core_batch_size)

Instances