[{'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:_, train_acc = modelF.evaluate(x_train_norm, y_train, verbose=0)\n_, test_acc = modelF.evaluate(x_test_norm, y_test, verbose=0)\nprint('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n# plot loss during training\nplt.rcParams['figure.figsize'] = (12.0, 9.0)\nplt.subplot(211)\nplt.title('Loss')\nplt.plot(Net4.history['loss'], label='train')\nplt.plot(Net4.history['val_loss'], label='test')\nplt.legend()\n# plot accuracy during training\nplt.subplot(212)\nplt.title('Accuracy')\nplt.plot(Net4.history['acc'], label='train')\nplt.plot(Net4.history['val_acc'], label='test')\nplt.legend()\nplt.show()"}, {'role': 'assistant', 'content': "['result_visualization']"}]