[{'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:X = np.linspace(0, 2 * np.pi, 1000).reshape(-1,1)\ny = np.sin(X)\nprint(X.shape, y.shape)\nmodel = Sequential()\nmodel.add(Dense(input_dim=1, output_dim=5))\nmodel.add(Activation(\'tanh\'))\nmodel.add(Dense(input_dim=5, output_dim=1))\nmodel.add(Activation(\'linear\'))\nmodel.compile(loss=\'mse\', optimizer=SGD(lr=0.05))\nprint(\'Training..\')\nloss = model.fit(X, y, nb_epoch=15000, validation_split=0.1,\nbatch_size=128, verbose=False)\nprint(loss.history[\'loss\'][-1])\nprint(\'Complete\')\npredictions = model.predict(X)\nplt.scatter(X, y)\nplt.plot(X, predictions, color=\'r\')\nplt.show()\nprint("MSE", mean_squared_error(predictions, y))'}, {'role': 'assistant', 'content': 'modelling'}]