[{'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:# Set the CNN model <br># my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out<br><br>model = Sequential()<br><br>model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = \'Same\', <br>                 activation =\'linear\', input_shape = (96,96,1)))<br>model.add(LeakyReLU(alpha=.001))<br>model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))<br>model.add(Dropout(0.25))<br><br>model.add(Flatten())<br>model.add(Dense(256, activation = "linear"))<br>model.add(LeakyReLU(alpha=.001))<br>model.add(Dropout(0.5))<br>model.add(Dense(128, activation = "linear"))<br>model.add(LeakyReLU(alpha=.001))<br>model.add(Dropout(0.5))<br>model.add(Dense(30))'}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]