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