class deeppavlov.core.models.component.Component, tracker: deeppavlov.models.go_bot.tracker.Tracker, network_parameters: Dict[str, Any], template_path: str, save_path: str, load_path: str = None, template_type: str = 'DefaultTemplate', word_vocab: deeppavlov.core.models.component.Component = None, bow_embedder: deeppavlov.core.models.component.Component = None, embedder: deeppavlov.core.models.component.Component = None, slot_filler: deeppavlov.core.models.component.Component = None, intent_classifier: deeppavlov.core.models.component.Component = None, database: deeppavlov.core.models.component.Component = None, api_call_action: str = None, use_action_mask: bool = False, debug: bool = False, **kwargs)[source]

The dialogue bot is based on, which introduces Hybrid Code Networks that combine an RNN with domain-specific knowledge and system action templates.

  • tokenizer – one of tokenizers from deeppavlov.models.tokenizers module.
  • tracker – dialogue state tracker from deeppavlov.models.go_bot.tracker.
  • network_parameters – initialization parameters for policy network (see GoalOrientedBotNetwork).
  • template_path – file with mapping between actions and text templates for response generation.
  • template_type – type of used response templates in string format.
  • word_vocab – vocabulary of input word tokens (DefaultVocabulary recommended).
  • bow_embedder – instance of one-hot word encoder BoWEmbedder.
  • embedder – one of embedders from deeppavlov.models.embedders module.
  • slot_filler – component that outputs slot values for a given utterance (DstcSlotFillingNetwork recommended).
  • intent_classifier – component that outputs intents probability distribution for a given utterance ( KerasClassificationModel recommended).
  • database – database that will be used during inference to perform api_call_action actions and get 'db_result' result ( Sqlite3Database recommended).
  • api_call_action – label of the action that corresponds to database api call (it must be present in your template_path file), during interaction it will be used to get 'db_result' from database.
  • use_action_mask – if True, network output will be applied with a mask over allowed actions.
  • debug – whether to display debug output.

Save the parameters of the model to a file.

class int, action_size: int, obs_size: int, learning_rate: float, end_learning_rate: float = None, decay_steps: int = 1000, decay_power: float = 1.0, dropout_rate: float = 0.0, l2_reg_coef: float = 0.0, dense_size: int = None, optimizer: str = 'AdamOptimizer', attention_mechanism: Dict = None, **kwargs)[source]

The GoalOrientedBotNetwork is a recurrent network that handles dialogue policy management. Inputs features of an utterance and predicts label of a bot action (classification task).

An LSTM with a dense layer for input features and a dense layer for it’s output. Softmax is used as an output activation function.

  • hidden_size – size of rnn hidden layer.
  • action_size – size of rnn output (equals to number of bot actions).
  • obs_size – input features’ size (must be equal to sum of output sizes of bow_embedder, embedder, intent_classifier, tracker.num_features plus size of context features(=6) and action_size).
  • learning_rate – learning rate during training.
  • end_learning_rate – if set, learning rate starts from learning rate value and decays polynomially to the value of end_learning_rate.
  • decay_steps – number of steps for learning rate to decay.
  • decay_power – power used to calculate learning rate decay for polynomial strategy.
  • dropout_rate – probability of weights dropping out.
  • l2_reg_coef – l2 regularization weight (applied to input and output layer).
  • dense_size – rnn input size.
  • optimizer – one of tf.train.Optimizer subclasses as a string.
  • attention_mechanism

    describes attention applied to embeddings of input tokens.

    • type – type of attention mechanism, possible values are 'general', 'bahdanau', 'light_general', 'light_bahdanau', 'cs_general' and 'cs_bahdanau'.
    • hidden_size – attention hidden state size.
    • max_num_tokens – maximum number of input tokens.
    • depth – number of averages used in constrained attentions ('cs_bahdanau' or 'cs_general').
    • action_as_key – whether to use action from previous timestep as key to attention.
    • intent_as_key – use utterance intents as attention key or not.
    • projected_align – whether to use output projection.
load(*args, **kwargs)[source]

Load model parameters from self.load_path

save(*args, **kwargs)[source]

Save model parameters to self.save_path

class deeppavlov.models.go_bot.tracker.Tracker[source]

An abstract class for trackers: a model that holds a dialogue state and generates state features.

class deeppavlov.models.go_bot.tracker.DefaultTracker(slot_names: List[str])[source]

Tracker that overwrites slots with new values. Features are binary indicators: slot is present/absent.

Parameters:slot_names – list of slots that should be tracked.
class deeppavlov.models.go_bot.tracker.FeaturizedTracker(slot_names: List[str])[source]

Tracker that overwrites slots with new values. Features are binary features (slot is present/absent) plus difference features (slot value is (the same)/(not the same) as before last update) and count features (sum of present slots and sum of changed during last update slots).

Parameters:slot_names – list of slots that should be tracked.