[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:epochs = 5\nif (test_data):\nbatch_size = 20\nsamples_per_epoch = batch_size\nnb_val_samples = batch_size\nelse:\nbatch_size = 128\nsamples_per_epoch = ((len(train_labels) // batch_size ) * batch_size)*2\nnb_val_samples = ((len(val_labels) // batch_size ) * batch_size)*2\nprint("Samples per epoch",samples_per_epoch)\nprint("Validation samples per epoch",nb_val_samples)\ndef train_model(epochs, batch_size, samples_per_epoch, nb_val_samples):\ncheckpoint = ModelCheckpoint(filepath="./model.h5", verbose=1, save_best_only=True)\ncallbacks_list = [checkpoint]\nhistory = model.fit_generator(\nfeed_data_generator(train_features, train_labels, batch_size),\nsamples_per_epoch=samples_per_epoch,\nnb_epoch=epochs,\nverbose=1,\ncallbacks = callbacks_list,\nvalidation_data = feed_data_generator(val_features, val_labels, batch_size),\nnb_val_samples = nb_val_samples)\nreturn history\ndef show_graph(history):\nplt.plot(history.history[\'loss\'])\nplt.title(\'model loss\')\nplt.ylabel(\'loss\')\nplt.xlabel(\'epoch\')\nplt.legend([\'train\', \'test\'], loc=\'upper left\')\nplt.show()\ndef evaluate_model():\nmetrics = model.evaluate(val_features, val_labels, batch_size=32, verbose=1)\nkeys = model.metrics_names\nprint(keys[0],":",metrics[0])\nprint(keys[1],":",metrics[1])\nmodel = get_model()\nhistory = train_model(epochs, batch_size, samples_per_epoch, nb_val_samples)\nevaluate_model()'}, {'role': 'assistant', 'content': 'modelling'}]