[{'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:"""\nDON\'T MODIFY ANYTHING IN THIS CELL\n"""\n%matplotlib inline\n%config InlineBackend.figure_format = \'retina\'\nimport tensorflow as tf\nimport pickle\nimport helper\nimport random\ntry:\nif batch_size:\npass\nexcept NameError:\nbatch_size = 64\nsave_model_path = \'./image_classification\'\nn_samples = 4\ntop_n_predictions = 3\ndef test_model():\n"""\nTest the saved model against the test dataset\n"""\ntest_features, test_labels = pickle.load(open(\'preprocess_training.p\', mode=\'rb\'))\nloaded_graph = tf.Graph()\nwith tf.Session(graph=loaded_graph) as sess:\nloader = tf.train.import_meta_graph(save_model_path + \'.meta\')\nloader.restore(sess, save_model_path)\nloaded_x = loaded_graph.get_tensor_by_name(\'x:0\')\nloaded_y = loaded_graph.get_tensor_by_name(\'y:0\')\nloaded_keep_prob = loaded_graph.get_tensor_by_name(\'keep_prob:0\')\nloaded_logits = loaded_graph.get_tensor_by_name(\'logits:0\')\nloaded_acc = loaded_graph.get_tensor_by_name(\'accuracy:0\')\ntest_batch_acc_total = 0\ntest_batch_count = 0\nfor train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\ntest_batch_acc_total += sess.run(\nloaded_acc,\nfeed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})\ntest_batch_count += 1\nprint(\'Testing Accuracy: {}\\n\'.format(test_batch_acc_total/test_batch_count))\nrandom_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\nrandom_test_predictions = sess.run(\ntf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\nfeed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\nhelper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\ntest_model()'}, {'role': 'assistant', 'content': "['helper_functions', 'evaluation', 'modelling', 'prediction']"}]