def __init__(
self,
tokenizer: Component,
tracker: Tracker,
network_parameters: Dict[str, Any],
template_path: str,
save_path: str,
load_path: str = None,
template_type: str = "DefaultTemplate",
word_vocab: Component = None,
bow_embedder: Component = None,
embedder: Component = None,
slot_filler: Component = None,
intent_classifier: Component = None,
database: Component = None,
api_call_action: str = None, # TODO: make it unrequired
use_action_mask: bool = False,
debug: bool = False,
**kwargs):
super().__init__(load_path=load_path, save_path=save_path, **kwargs)
self.tokenizer = tokenizer
self.tracker = tracker
self.bow_embedder = bow_embedder
self.embedder = embedder
self.slot_filler = slot_filler
self.intent_classifier = intent_classifier
self.use_action_mask = use_action_mask
self.debug = debug
self.word_vocab = word_vocab
template_path = expand_path(template_path)
template_type = getattr(templ, template_type)
log.info("[loading templates from {}]".format(template_path))
self.templates = templ.Templates(template_type).load(template_path)
self.n_actions = len(self.templates)
log.info("{} templates loaded".format(self.n_actions))
self.database = database
self.api_call_id = None
if api_call_action is not None:
self.api_call_id = self.templates.actions.index(api_call_action)
self.intents = []
if callable(self.intent_classifier):
# intent_classifier returns y_probas
self.intents = self.intent_classifier.get_main_component().classes
network_parameters['load_path'] = load_path
network_parameters['save_path'] = save_path
self.network = self._init_network(network_parameters)
self.reset()
def __init__(self,
template_path,
network_parameters,
tokenizer,
tracker,
template_type: str = "BaseTemplate",
database=None,
api_call_action=None,
bow_embedder=None,
embedder=None,
slot_filler=None,
intent_classifier=None,
use_action_mask=False,
debug=False,
save_path=None,
word_vocab=None,
vocabs=None,
**kwargs):
super().__init__(save_path=save_path, mode=kwargs['mode'])
self.tokenizer = tokenizer
self.tracker = tracker
self.bow_embedder = bow_embedder
self.embedder = embedder
self.slot_filler = slot_filler
self.intent_classifier = intent_classifier
self.use_action_mask = use_action_mask
self.debug = debug
self.word_vocab = word_vocab or vocabs['word_vocab']
template_path = expand_path(template_path)
template_type = getattr(templ, template_type)
log.info("[loading templates from {}]".format(template_path))
self.templates = templ.Templates(template_type).load(template_path)
self.n_actions = len(self.templates)
log.info("{} templates loaded".format(self.n_actions))
self.database = database
self.api_call_id = self.templates.actions.index(api_call_action)
self.intents = []
if callable(self.intent_classifier):
# intent_classifier returns y_labels, y_probs
self.intents = list(self.intent_classifier(["hi"])[1][0].keys())
self.network = self._init_network(network_parameters)
self.reset()
def __init__(self,
tokenizer: Component,
tracker: Tracker,
template_path: str,
save_path: str,
hidden_size: int = 128,
obs_size: int = None,
action_size: int = None,
dropout_rate: float = 0.,
l2_reg_coef: float = 0.,
dense_size: int = None,
attention_mechanism: dict = None,
network_parameters: Dict[str, Any] = {},
load_path: str = None,
template_type: str = "DefaultTemplate",
word_vocab: Component = None,
bow_embedder: Component = None,
embedder: Component = None,
slot_filler: Component = None,
intent_classifier: Component = None,
database: Component = None,
api_call_action: str = None, # TODO: make it unrequired
use_action_mask: bool = False,
debug: bool = False,
**kwargs):
if any(p in network_parameters for p in self.DEPRECATED):
log.warning(f"parameters {self.DEPRECATED} are deprecated,"
" for learning rate schedule documentation see"
" deeppavlov.core.models.lr_scheduled_tf_model"
" or read gitub tutorial on super convergence.")
if 'learning_rate' in network_parameters:
kwargs['learning_rate'] = network_parameters.pop('learning_rate')
super().__init__(load_path=load_path, save_path=save_path, **kwargs)
self.tokenizer = tokenizer
self.tracker = tracker
self.bow_embedder = bow_embedder
self.embedder = embedder
self.slot_filler = slot_filler
self.intent_classifier = intent_classifier
self.use_action_mask = use_action_mask
self.debug = debug
self.word_vocab = word_vocab
template_path = expand_path(template_path)
template_type = getattr(templ, template_type)
log.info("[loading templates from {}]".format(template_path))
self.templates = templ.Templates(template_type).load(template_path)
self.n_actions = len(self.templates)
log.info("{} templates loaded".format(self.n_actions))
self.database = database
self.api_call_id = None
if api_call_action is not None:
self.api_call_id = self.templates.actions.index(api_call_action)
self.intents = []
if callable(self.intent_classifier):
self.intents = self.intent_classifier.get_main_component().classes
new_network_parameters = {
'hidden_size': hidden_size,
'action_size': action_size,
'obs_size': obs_size,
'dropout_rate': dropout_rate,
'l2_reg_coef': l2_reg_coef,
'dense_size': dense_size,
'attn': attention_mechanism
}
if 'attention_mechanism' in network_parameters:
network_parameters['attn'] = network_parameters.pop('attention_mechanism')
new_network_parameters.update(network_parameters)
self._init_network(**new_network_parameters)
self.reset()
def __init__(self,
tokenizer: Component,
tracker: FeaturizedTracker,
template_path: str,
save_path: str,
hidden_size: int = 128,
obs_size: int = None,
action_size: int = None,
dropout_rate: float = 0.,
l2_reg_coef: float = 0.,
dense_size: int = None,
attention_mechanism: dict = None,
network_parameters: Optional[Dict[str, Any]] = None,
load_path: str = None,
template_type: str = "DefaultTemplate",
word_vocab: Component = None,
bow_embedder: Component = None,
embedder: Component = None,
slot_filler: Component = None,
intent_classifier: Component = None,
database: Component = None,
api_call_action: str = None,
use_action_mask: bool = False,
debug: bool = False,
**kwargs) -> None:
network_parameters = network_parameters or {}
if any(p in network_parameters for p in self.DEPRECATED):
log.warning(f"parameters {self.DEPRECATED} are deprecated,"
f" for learning rate schedule documentation see"
f" deeppavlov.core.models.lr_scheduled_tf_model"
f" or read a github tutorial on super convergence.")
if 'learning_rate' in network_parameters:
kwargs['learning_rate'] = network_parameters.pop('learning_rate')
super().__init__(load_path=load_path, save_path=save_path, **kwargs)
self.tokenizer = tokenizer
self.bow_embedder = bow_embedder
self.embedder = embedder
self.slot_filler = slot_filler
self.intent_classifier = intent_classifier
self.use_action_mask = use_action_mask
self.debug = debug
self.word_vocab = word_vocab
template_path = expand_path(template_path)
template_type = getattr(templ, template_type)
log.info(f"[loading templates from {template_path}]")
self.templates = templ.Templates(template_type).load(template_path)
self.n_actions = len(self.templates)
log.info(f"{self.n_actions} templates loaded.")
self.default_tracker = tracker
self.dialogue_state_tracker = DialogueStateTracker(
tracker.slot_names, self.n_actions, hidden_size, database)
self.api_call_id = -1
if api_call_action is not None:
self.api_call_id = self.templates.actions.index(api_call_action)
self.intents = []
if isinstance(self.intent_classifier, Chainer):
self.intents = self.intent_classifier.get_main_component().classes
new_network_parameters = {
'hidden_size': hidden_size,
'action_size': action_size,
'obs_size': obs_size,
'dropout_rate': dropout_rate,
'l2_reg_coef': l2_reg_coef,
'dense_size': dense_size,
'attn': attention_mechanism
}
if 'attention_mechanism' in network_parameters:
network_parameters['attn'] = network_parameters.pop(
'attention_mechanism')
new_network_parameters.update(network_parameters)
self._init_network(**new_network_parameters)
self.multiple_user_state_tracker = MultipleUserStateTracker()
self.reset()
def __init__(
self,
tokenizer: Component,
tracker: Tracker,
template_path: str,
save_path: str,
hidden_size: int,
obs_size: int = None,
action_size: int = None,
dropout_rate: float = 0.,
l2_reg_coef: float = 0.,
dense_size: int = None,
attention_mechanism: dict = None,
load_path: str = None,
template_type: str = "DefaultTemplate",
word_vocab: Component = None,
bow_embedder: Component = None,
embedder: Component = None,
slot_filler: Component = None,
intent_classifier: Component = None,
database: Component = None,
api_call_action: str = None, # TODO: make it unrequired
use_action_mask: bool = False,
debug: bool = False,
**kwargs):
super().__init__(load_path=load_path, save_path=save_path, **kwargs)
self.tokenizer = tokenizer
self.tracker = tracker
self.bow_embedder = bow_embedder
self.embedder = embedder
self.slot_filler = slot_filler
self.intent_classifier = intent_classifier
self.use_action_mask = use_action_mask
self.debug = debug
self.word_vocab = word_vocab
template_path = expand_path(template_path)
template_type = getattr(templ, template_type)
log.info("[loading templates from {}]".format(template_path))
self.templates = templ.Templates(template_type).load(template_path)
self.n_actions = len(self.templates)
log.info("{} templates loaded".format(self.n_actions))
self.database = database
self.api_call_id = None
if api_call_action is not None:
self.api_call_id = self.templates.actions.index(api_call_action)
self.intents = []
if callable(self.intent_classifier):
self.intents = self.intent_classifier.get_main_component().classes
self._init_network(hidden_size=hidden_size,
action_size=action_size,
obs_size=obs_size,
dropout_rate=dropout_rate,
l2_reg_coef=l2_reg_coef,
dense_size=dense_size,
attn=attention_mechanism)
self.reset()
