[{'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:from keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization, GlobalMaxPooling2D\nfrom keras import backend as K\n\ndef root_mean_squared_error(y_true, y_pred):\n        return K.sqrt(K.mean(K.square(y_pred - y_true))) \n\nmodel = Sequential()\nmodel.add(Conv2D(512, (3, 3), input_shape=(IMG_SHAPE[0], IMG_SHAPE[1], 3)))\nmodel.add(Conv2D(256, (3, 3)))\n#model.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(256, (3, 3)))\nmodel.add(Conv2D(128, (3, 3)))\n#model.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(128, (3, 3)))\nmodel.add(Conv2D(128, (3, 3)))\n#model.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(GlobalMaxPooling2D())\n\n\nmodel.add(Dropout(0.25))\nmodel.add(Dense(128))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\nmodel.add(Dense(128))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\nmodel.add(Dense(128))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\nmodel.add(Dense(37))\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adamax', metrics=[root_mean_squared_error])\nmodel.summary()"}, {'role': 'assistant', 'content': "['modelling']"}]