[{'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:import matplotlib.pyplot as plt\n\n# Create count of the number of epochs\nepoch_count = range(1, len(history.history['loss']) + 1)\n\n# Visualize learning curve. Here learning curve is not ideal. It should be much smoother as it decreases.\n#As mentioned before, altering different hyper parameters especially learning rate can have a positive impact\n#on accuracy and learning curve.\nplt.plot(epoch_count, history.history['loss'], 'r--')\nplt.plot(epoch_count, history.history['val_loss'], 'b-')\nplt.legend(['Training Loss', 'Validation Loss'])\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.show()"}, {'role': 'assistant', 'content': "['result_visualization']"}]