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

        data_dir=FLAGS.data,
        per_core_batch_size=per_core_batch_size)

    callbacks = [
        LearningRateBatchScheduler(schedule=learning_rate_schedule),
        eval_utils.TensorBoardWithValidation(
            log_dir=model_dir,
            validation_imagenet_input=imagenet_validation,
            validation_steps=VALIDATION_STEPS,
            validation_epochs=[30, 60, 90],
            eval_top_k_accuracy=FLAGS.eval_top_5_accuracy),
    ]

    model.fit(imagenet_train.input_fn,
              epochs=EPOCHS,
              steps_per_epoch=TRAINING_STEPS_PER_EPOCH,

Instances