def finetune_classify(self, test_dataset, nclasses=2, num_workers=8, load_weights_path=None):
    ' Uses the finetuned model to predict on a test dataset. \n                :param test_dataset: X, tf.constant tensor for inference\n                :param nclasses: number of classes \n                :param num_workers: number of workers to use for multiprocess data loading\n                :return: array of softmaxed prediction probabilities\n                :param load_weights_path: optional filepath from which to try to load weights\n        '
    finetune_model = self._load_finetune_model(nclasses=nclasses, weights_path=load_weights_path)
    return finetune_model.predict_generator(test_dataset, use_multiprocessing=True, workers=num_workers)
Uses the finetuned model to predict on a test dataset. 
:param test_dataset: X, tf.constant tensor for inference
:param nclasses: number of classes 
:param num_workers: number of workers to use for multiprocess data loading
:return: array of softmaxed prediction probabilities
:param load_weights_path: optional filepath from which to try to load weights
def finetune_classify(self, test_dataset, nclasses=2, num_workers=8, load_weights_path=None):
    ' Uses the finetuned model to predict on a test dataset. \n                :param test_dataset: X, tf.constant tensor for inference\n                :param nclasses: number of classes \n                :param num_workers: number of workers to use for multiprocess data loading\n                :return: array of softmaxed prediction probabilities\n                :param load_weights_path: optional filepath from which to try to load weights\n        '
    finetune_model = self._load_finetune_model(nclasses=nclasses, weights_path=load_weights_path)
    return finetune_model.predict_generator(test_dataset, use_multiprocessing=True, workers=num_workers)