[{'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:for iteration in range(1, 20):\nprint()\nprint(\'-\' * 50)\nprint(\'Iteration\', iteration)\nmodel.fit(x_train, y_train,\nbatch_size = BATCH_SIZE,\nepochs = 1,\nvalidation_data = (x_val, y_val))\nfor i in range(10):\nind = np.random.randint(0, len(x_val))\nrowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]\npreds = model.predict_classes(rowx, verbose = 0)\nq = ctable.decode(rowx[0])\ncorrect = ctable.decode(rowy[0])\nguess = ctable.decode(preds[0], calc_argmax = False)\nprint(\'Q\', q[::-1] if INVERT else q)\nprint(\'T\', correct)\nif correct == guess:\nprint(colors.ok + \'☑\' + colors.close, end = " ")\nelse:\nprint(colors.fail + \'☒\' + colors.close, end = " ")\nprint(guess)\nprint(\'---\')'}, {'role': 'assistant', 'content': "['modelling', 'prediction']"}]