[{'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:# Initialising the CNN\nclassifier = Sequential()\n\n# Step 1 - Convolution\nclassifier.add(Convolution2D(32, 3, 3, input_shape = (1,50,50), activation = 'relu'))\n\n# Step 2 - Pooling\nclassifier.add(MaxPooling2D(pool_size = (2, 2)))\n\n# Adding a second convolutional layer\nclassifier.add(Convolution2D(32, 3, 3, activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size = (2, 2)))\n\n\n\n# Adding a third convolutional layer\nclassifier.add(Convolution2D(64, 3, 3, activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size = (2, 2)))\n\n\n\n\n# Step 3 - Flattening\nclassifier.add(Flatten())\n\n# Step 4 - Full connection\nclassifier.add(Dense(output_dim = 64, activation = 'relu'))\nclassifier.add(Dropout(0.4))\nclassifier.add(Dense(output_dim = 2, activation = 'sigmoid'))\n\n"}, {'role': 'assistant', 'content': "['modelling']"}]