Hi All,
I am facing issue as "AttributeError: ‘function’ object has no attribute ‘train_dialogue_model’. I suspect train_dialogue_model function doesn’t exist this may be causing the issue. Can any one please confirm and provide me the alternative function for the same.
Version : 1.0.0rc2
Error: C:\Users\HP\horoscope_bot>python train_online.py 2019-08-18 10:11:22.030464: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 WARNING:tensorflow:From C:\Users\HP\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. 2019-08-18 10:11:22 WARNING tensorflow - From C:\Users\HP\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. INFO:tensorflow:Restoring parameters from ./models/nlu/default/horoscopebot\component_5_EmbeddingIntentClassifier.ckpt 2019-08-18 10:11:22 INFO tensorflow - Restoring parameters from ./models/nlu/default/horoscopebot\component_5_EmbeddingIntentClassifier.ckpt Traceback (most recent call last): File “train_online.py”, line 32, in agent = train_agent(interpreter) File “train_online.py”, line 16, in train_agent return train.train_dialogue_model(domain_file=“horoscope_domain.yml”, AttributeError: ‘function’ object has no attribute ‘train_dialogue_model’
Code:
from future import absolute_import from future import division from future import print_function from future import unicode_literals
import logging
from rasa_core import utils, train from rasa_core.training import interactive from rasa_core.interpreter import NaturalLanguageInterpreter
logger = logging.getLogger(name)
def train_agent(interpreter): return train.train_dialogue_model(domain_file=“horoscope_domain.yml”, stories_file="./data/stories.md", output_path="./models/dialogue", nlu_model_path=interpreter, endpoints=“endpoints.yml”, max_history=2, kwargs={“batch_size”: 50, “epochs”: 200, “max_training_samples”: 300 })
if name == ‘main’: utils.configure_colored_logging(loglevel=“DEBUG”) nlu_model_path = “./models/nlu/default/horoscopebot” interpreter = NaturalLanguageInterpreter.create(nlu_model_path) agent = train_agent(interpreter) interactive.serve_agent(agent)