[{'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:rate = 0.001\nx = tf.placeholder(tf.float32, (None, 32, 32, 3))\ny = tf.placeholder(tf.int32, (None))\none_hot_y = tf.one_hot(y, 43)\nlogits = LeNet(x)\ncross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\nloss_operation = tf.reduce_mean(cross_entropy)\noptimizer = tf.train.AdamOptimizer(learning_rate=rate)\ntraining_operation = optimizer.minimize(loss_operation)\ncorrect_prediction = tf.equal(tf.argmax(logits, axis=1), tf.argmax(one_hot_y, axis=1))\naccuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\nsaver = tf.train.Saver()\ndef evaluate(X_data, y_data):\nnum_examples = len(X_data)\ntotal_accuracy = 0\nsess = tf.get_default_session()\nfor offset in range(0, num_examples, BATCH_SIZE):\nbatch_x, batch_y = X_train[offset: offset + BATCH_SIZE], y_train[offset: offset + BATCH_SIZE]\naccuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\ntotal_accuracy += (accuracy * len(batch_x))\nreturn total_accuracy / num_examples'}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]