[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:init = tf.global_variables_initializer()\nsess = tf.Session()\nsess.run(init)\ntest_data = {X: mnist.test.images, Y_: mnist.test.labels, pkeep: 1.0}\ndef training_step(i, update_test_data, update_train_data):\nmax_learning_rate = 0.003\nmin_learning_rate = 0.0001\ndecay_speed = 2000.0 # 0.003-0.0001-2000=>0.9826 done in 5000 iterations\nlearning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed)\nbatch_X, batch_Y = mnist.train.next_batch(100)\nif update_train_data:\na, c, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X, Y_: batch_Y, pkeep: 1.0})\nprint(str(i) + ": accuracy:" + str(a) + " loss: " + str(c) + " (lr:" + str(learning_rate) + ")")\nif update_test_data:\na, c = sess.run([accuracy, cross_entropy], test_data)\nprint(str(i) + ": ********* epoch " + str(i*100//mnist.train.images.shape[0]+1) + " ********* test accuracy:" + str(a) + " test loss: " + str(c))\nsess.run(train_step, {X: batch_X, Y_: batch_Y, lr: learning_rate, pkeep: 0.75})'}, {'role': 'assistant', 'content': 'modelling'}]