def evaluate(images_filename, labels_filename, names_filename): images = deep_utils.load_image_data(images_filename) labels = deep_utils.load_labels(labels_filename, 2) names = np.load(names_filename)['arr_0'] model = load_model(MODEL_FILENAME) return model.evaluate(images, labels)
def load_train_datasets(self, fold_id): ids = deep_utils.load_ids(self.train_id_filenames[fold_id]) data = deep_utils.load_image_data(self.data_filename) data = np.take(data, ids, axis=0) labels = deep_utils.load_labels(self.labels_filename, NUM_CLASSES) labels = np.take(labels, ids, axis=0) return (data, labels)
def load_pretrain_datasets(self): data = deep_utils.load_image_data(self.pretrain_data_filename) labels = deep_utils.load_labels(self.pretrain_labels_filename, NUM_CLASSES) return (data, labels)
def compute_number_of_elements(self): labels = deep_utils.load_labels(self.labels_filename, NUM_CLASSES) return labels.shape[0]