[{'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 get_model(time_len=1):\nchannels, height, width = 3, 45, 160  # image format\nmodel = Sequential()\nmodel.add(Lambda(lambda x: x/255. - 0.5,\ninput_shape=(height, width, channels),\noutput_shape=(height, width, channels)))\nmodel.add(Convolution2D(32, 3, 3))\nmodel.add(Activation(\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Convolution2D(32, 3, 3))\nmodel.add(Activation(\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Convolution2D(64, 3, 3))\nmodel.add(Activation(\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Flatten()) # converts 3D feature maps into 1D vectors\nmodel.add(Dense(64))\nmodel.add(Activation(\'relu\'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1))\nmodel.compile(optimizer="adam", loss="mse", metrics=[\'mse\'])\nreturn model\nif(debug):\nprint("Model setup")'}, {'role': 'assistant', 'content': "['modelling']"}]