[{'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:cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))\ntrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\ncorrect_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\nsess.run(tf.initialize_all_variables())\nfor i in range(20000):\nbatch = mnist.train.next_batch(50)\nif i%100 == 0:\ntrain_accuracy = accuracy.eval(feed_dict={\nx:batch[0], y_: batch[1], keep_prob: 1.0})\nprint("step %d, training accuracy %g"%(i, train_accuracy))\ntrain_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\nprint("test accuracy %g"%accuracy.eval(feed_dict={\nx: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))'}, {'role': 'assistant', 'content': 'modelling'}]