[{'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:model = Sequential()\nmodel.add(Convolution2D(32, 3, 3, input_shape=X_train.shape[1:], activation=\'relu\', border_mode=\'same\'))\nmodel.add(Activation(\'relu\'))\nmodel.add(Convolution2D(32, 3, 3, activation=\'relu\', border_mode=\'same\'))\nmodel.add(Dropout(0.2))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Convolution2D(64, 3, 3, activation=\'relu\', border_mode=\'same\'))\nmodel.add(Dropout(0.2))\nmodel.add(Convolution2D(64, 3, 3, activation=\'relu\', border_mode=\'same\'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(1024, activation=\'relu\', W_constraint=maxnorm(3)))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation=\'softmax\'))\nepochs = 30\nmodel.compile(optimizer="adam", loss=\'categorical_crossentropy\', metrics=[\'accuracy\'])\nprint(model.summary())'}, {'role': 'assistant', 'content': 'modelling'}]