[{'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:def create_model():\n    \'\'\'\n    Create a neural network\n    \'\'\'\n    initializer = RandomNormal(mean=0.0, stddev=0.05, seed=None)\n\n    model = Sequential()\n    model.add(Dense(512, input_dim=X_train.shape[1], activation="relu", kernel_initializer=initializer))\n    model.add(Dropout(0.4))\n    model.add(Dense(512, input_dim=X_train.shape[1], activation="relu", kernel_initializer=initializer))\n    model.add(Dropout(0.4))\n    model.add(Dense(512, input_dim=X_train.shape[1], activation="relu", kernel_initializer=initializer))\n    model.add(Dropout(0.4))\n    model.add(Dense(1, activation="linear", kernel_initializer=initializer))\n    adam = Adam(lr=1e-3, decay=1e-3)\n\n    # Compile model\n    model.compile(loss="mean_squared_error", optimizer=adam, metrics=[rmse, rmspe])\n\n    return model\''}, {'role': 'assistant', 'content': "['modelling']"}]