[{'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:L1 = 3 #Number of neurons in 1st layer\nL2 = 3 #Number of neurons in 2nd layer\nL3 = 3 #Number of neurons in 2nd layer\nW_fc1 = tf.Variable(tf.random_uniform([Xfeatures,L1])) # [input_features,Number of neurons])\nb_fc1 = tf.Variable(tf.constant(0.1,shape=[L1]))\nW_fc2 = tf.Variable(tf.random_uniform([L1,L2])) # [Number of neurons in preceding layer,Number of neurons])\nb_fc2 = tf.Variable(tf.constant(0.1,shape=[L2]))\nW_fc3 = tf.Variable(tf.random_uniform([L2,L3])) # [Number of neurons in preceding layer,Number of neurons])\nb_fc3 = tf.Variable(tf.constant(0.1,shape=[L3]))\nW_fO= tf.Variable(tf.random_uniform([L3,Yfeatures])) #  [Number of neurons in preceding layer,output_features])\nb_fO = tf.Variable(tf.constant(0.1,shape=[Yfeatures]))'}, {'role': 'assistant', 'content': "['modelling']"}]