[{'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:%%time\nNN_model = Sequential()\n\n# The Input Layer :\nNN_model.add(Dense(128, kernel_initializer='normal',input_dim = train_x_new.shape[1], activation='relu'))\n\n# The Hidden Layers :\nNN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\nNN_model.add(Dropout(0.3))\nNN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\nNN_model.add(Dropout(0.3))\nNN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\nNN_model.add(Dropout(0.3))\n# The Output Layer :\nNN_model.add(Dense(1, kernel_initializer='normal',activation='linear'))\n\n# Compile the network :\nNN_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])\nNN_model.summary()\n\nhistory = NN_model.fit(train_x_new,train_y_new, epochs=50, batch_size=64,  verbose=1, validation_split=0.2)\nstart_point = 150-100\nr = range(start_point,150)\nplt.figure(figsize=(16,8))\nplt.plot( history.history['loss']  ,'r' ,label ='Train')\nplt.plot( history.history['val_loss']  , 'g' , label = 'Test' )\nplt.legend(fontsize='x-large')\nvalid_0_error_new = history.history['loss'][-1]\nvalid_1_error_new = history.history['val_loss'][-1]\nscore_diff(valid_0_error , valid_1_error , valid_0_error_new , valid_1_error_new)\nvalid_0_error = valid_0_error_new\nvalid_1_error = valid_1_error_new\n"}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]