def create_policy(self, featurizer, priority): # use standard featurizer from EmbeddingPolicy, # since it is using FullDialogueTrackerFeaturizer p = EmbeddingPolicy(priority=priority, attn_before_rnn=True, attn_after_rnn=True) return p
def train_dialogue_embed(domain_file="mobile_domain.yml", model_path="models/dialogue_embed", training_data_file="data/mobile_edit_story.md"): fallback = FallbackPolicy(fallback_action_name="action_default_fallback", nlu_threshold=0.5, core_threshold=0.3) agent = Agent(domain_file, policies=[ MemoizationPolicy(max_history=5), EmbeddingPolicy(epochs=100), fallback ]) training_data = agent.load_data(training_data_file) agent.train(training_data, validation_split=0.2) agent.persist(model_path) return agent
def train_dialogue(domain_file='./config/domain/domain.yml', training_data_file='./config/stories/stories.md', model_path='./models/dialogue'): fallback = FallbackPolicy(fallback_action_name="utter_default", core_threshold=0.2, nlu_threshold=0.1) agent = Agent(domain_file, policies=[ MemoizationPolicy(), KerasPolicy(), fallback, FormPolicy(), EmbeddingPolicy(epochs=100) ]) agent.visualize(training_data_file, output_file="graph.html", max_history=4) training_data = agent.load_data( training_data_file) # augmentation_factor=0 agent.train(training_data) agent.persist(model_path) return agent
def create_policy(self, featurizer, priority): # use standard featurizer from EmbeddingPolicy, # since it is using FullDialogueTrackerFeaturizer p = EmbeddingPolicy(priority=priority, **tf_defaults()) return p
def create_policy(self, featurizer): # use standard featurizer from EmbeddingPolicy, # since it is using FullDialogueTrackerFeaturizer p = EmbeddingPolicy() return p
def create_policy(self, featurizer): # use standard featurizer from EmbeddingPolicy, # since it is using FullDialogueTrackerFeaturizer p = EmbeddingPolicy(attn_before_rnn=False, attn_after_rnn=False) return p
def create_policy(self, featurizer): p = EmbeddingPolicy(featurizer) return p