[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:test_batch_size = 256\ndef print_test_accuracy(show_example_errors = False,\nshow_confusion_matrix = False):\nnum_test = len(data.test.images)\ncls_pred = np.zeros(shape=num_test, dtype=np.int)\ni = 0\nwhile i < num_test:\nj = min(i + test_batch_size, num_test)\nimages = data.test.images[i:j, :]\nlabels = data.test.labels[i:j, :]\nfeed_dict = {x:images,\ny_true: labels}\ncls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\ni = j\ncls_true = data.test.cls\ncorrect = (cls_true == cls_pred)\ncorrect_sum = correct.sum()\nacc = float(correct_sum) / num_test\nmsg = "Accuracy on Test-Set: {0:.1%} ({1} /{2})"\nprint(msg.format(acc, correct_sum, num_test))\nif show_example_errors:\nprint("Example errors:")\nplot_example_errors(cls_pred=cls_pred, correct=correct)\nif show_confusion_matrix:\nprint("Confusion Matrix:")\nplot_confusion_matrix(cls_pred=cls_pred)'}, {'role': 'assistant', 'content': "['evaluation']"}]