[{'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:model = models.Sequential()\n\nmodel.add(layers.Conv2D(32, (3, 3), input_shape=(img_width, img_height, 3)))\nmodel.add(layers.Activation('relu'))\nmodel.add(layers.MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(layers.Conv2D(32, (3, 3)))\nmodel.add(layers.Activation('relu'))\nmodel.add(layers.MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(layers.Conv2D(64, (3, 3)))\nmodel.add(layers.Activation('relu'))\nmodel.add(layers.MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(64))\nmodel.add(layers.Activation('relu'))\nmodel.add(layers.Dropout(0.5))\nmodel.add(layers.Dense(1))\nmodel.add(layers.Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='rmsprop',\n              metrics=['accuracy'])\n\nmodel.summary()"}, {'role': 'assistant', 'content': "['modelling']"}]