[{'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:convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\nconvnet = max_pool_2d(convnet, 5)\n\nconvnet = fully_connected(convnet, 1024, activation='relu')\nconvnet = dropout(convnet, 0.8)\n\nconvnet = fully_connected(convnet, 2, activation='softmax')\nconvnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n\nmodel = tflearn.DNN(convnet, tensorboard_dir='log')\n\n\n\nif os.path.exists('{}.meta'.format(MODEL_NAME)):\n    model.load(MODEL_NAME)\n    print('model loaded!')\n\ntrain = train_data[:-500]\ntest = train_data[-500:]\n\nX = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\nY = [i[1] for i in train]\n\ntest_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\ntest_y = [i[1] for i in test]\n\nmodel.fit({'input': X}, {'targets': Y}, n_epoch=4, validation_set=({'input': test_x}, {'targets': test_y}), \n    snapshot_step=500, show_metric=True, run_id=MODEL_NAME)"}, {'role': 'assistant', 'content': 'modelling'}]