def __init__(
self,
name: Text,
config: Dict[Text, Any],
data_signature: Dict[Text, Dict[Text, List[FeatureSignature]]],
label_data: RasaModelData,
) -> None:
super().__init__(
name=name,
random_seed=config[RANDOM_SEED],
tensorboard_log_dir=config[TENSORBOARD_LOG_DIR],
tensorboard_log_level=config[TENSORBOARD_LOG_LEVEL],
checkpoint_model=config[CHECKPOINT_MODEL],
)
self.config = config
self.data_signature = data_signature
self.label_signature = label_data.get_signature()
self._check_data()
label_batch = label_data.prepare_batch()
self.tf_label_data = self.batch_to_model_data_format(
label_batch, self.label_signature)
# set up tf layers
self._tf_layers: Dict[Text, tf.keras.layers.Layer] = {}
def predict(self, predict_data: RasaModelData) -> Dict[Text, tf.Tensor]:
if self._predict_function is None:
logger.debug("There is no tensorflow prediction graph.")
self.build_for_predict(predict_data)
# Prepare a single batch of the size of the input
batch_in = predict_data.prepare_batch()
self._training = False # needed for eager mode
return self._predict_function(batch_in)
def __init__(
self,
data_signature: Dict[Text, List[FeatureSignature]],
config: Dict[Text, Any],
max_history_tracker_featurizer_used: bool,
label_data: RasaModelData,
) -> None:
super().__init__(
name="TED",
random_seed=config[RANDOM_SEED],
tensorboard_log_dir=config[TENSORBOARD_LOG_DIR],
tensorboard_log_level=config[TENSORBOARD_LOG_LEVEL],
)
self.config = config
self.max_history_tracker_featurizer_used = max_history_tracker_featurizer_used
# data
self.data_signature = data_signature
self._check_data()
self.predict_data_signature = {
feature_name: features
for feature_name, features in data_signature.items()
if DIALOGUE in feature_name
}
# optimizer
self.optimizer = tf.keras.optimizers.Adam()
self.all_labels_embed = None
label_batch = label_data.prepare_batch()
self.tf_label_data = self.batch_to_model_data_format(
label_batch, label_data.get_signature()
)
# metrics
self.action_loss = tf.keras.metrics.Mean(name="loss")
self.action_acc = tf.keras.metrics.Mean(name="acc")
self.metrics_to_log += ["loss", "acc"]
# set up tf layers
self._tf_layers: Dict[Text : tf.keras.layers.Layer] = {}
self._prepare_layers()
def __init__(
self,
data_signature: Dict[Text, List[FeatureSignature]],
label_data: RasaModelData,
index_label_id_mapping: Optional[Dict[int, Text]],
index_tag_id_mapping: Optional[Dict[int, Text]],
config: Dict[Text, Any],
) -> None:
super().__init__(
name="CRFTransformer",
random_seed=config[RANDOM_SEED],
tensorboard_log_dir=config[TENSORBOARD_LOG_DIR],
tensorboard_log_level=config[TENSORBOARD_LOG_LEVEL],
)
self.config = config
self.data_signature = data_signature
self._check_data()
self.predict_data_signature = {
feature_name: features
for feature_name, features in data_signature.items()
if TEXT in feature_name
}
label_batch = label_data.prepare_batch()
self.tf_label_data = self.batch_to_model_data_format(
label_batch, label_data.get_signature())
self._num_intents = len(index_label_id_mapping
) if index_label_id_mapping is not None else 0
self._num_tags = len(
index_tag_id_mapping) if index_tag_id_mapping is not None else 0
# tf objects, training
self._prepare_layers()
self._set_optimizer(tf.keras.optimizers.Adam(config[LEARNING_RATE]))
self._create_metrics()
self._update_metrics_to_log()
