[{'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:out_model = models.Sequential()\nout_model.add(layers.Reshape((64, 64, 1), input_shape=trn_image.shape[1:]))\nout_model.add(layers.Conv2D(16, (3, 3), padding='valid', activation='relu'))\nout_model.add(layers.MaxPool2D((2, 2)))\nout_model.add(layers.Conv2D(32, (3, 3), padding='valid', activation='relu'))\nout_model.add(layers.MaxPool2D((2, 2)))\nout_model.add(layers.Conv2D(64, (3, 3), padding='valid', activation='relu'))\nout_model.add(layers.MaxPool2D((2, 2)))\nout_model.add(layers.Conv2D(128, (3, 3), padding='valid', activation='relu'))\nout_model.add(layers.MaxPool2D((2, 2)))\nout_model.add(layers.GlobalAveragePooling2D())\nout_model.add(layers.Dense(32, activation='relu'))\nout_model.add(layers.Dense(1, activation='sigmoid'))\nout_model.compile(optimizer='adam', \n                  loss='binary_crossentropy', \n                  metrics=['binary_accuracy'])\nout_model.summary()"}, {'role': 'assistant', 'content': 'modelling'}]