[{'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 make_model(size=(256,256)):\n    def make_cnn(kernel_nums, x):\n        for n in kernel_nums:\n            x = Conv2D(n, kernel_size=3, strides=1, activation='relu', padding='same')(x)\n            x = MaxPooling2D(pool_size=2, strides=2, padding='same')(x)\n            x = BatchNormalization()(x)\n            x = SpatialDropout2D(0.3)(x)\n        return Flatten()(x)\n    inp = Input((size[0],size[1],1))\n    kernel_nums = [64, 64,128,128,256,256,512,512]\n    scaled = inp\n    cnn_outs = []\n    for i in range(6):\n        scaled = AveragePooling2D(pool_size=2**i, strides=2**i)(inp)\n        cnn_outs.append(make_cnn(kernel_nums[:len(kernel_nums)-i], scaled))\n    x = concatenate(cnn_outs)\n    x = Dense(512, activation='relu')(x)\n    x = Dropout(0.3)(x)\n    out = Dense(1, activation='sigmoid')(x)\n    return Model(inp, out)"}, {'role': 'assistant', 'content': "['modelling']"}]