def _build_tf_train_graph(
self,
session_data: SessionDataType) -> Tuple["tf.Tensor", "tf.Tensor"]:
# get in tensors from generator
self.batch_in = self._iterator.get_next()
# convert encoded all labels into the batch format
label_batch = train_utils.prepare_batch(self._label_data)
# convert batch format into sparse and dense tensors
batch_data, _ = train_utils.batch_to_session_data(
self.batch_in, session_data)
label_data, _ = train_utils.batch_to_session_data(
label_batch, self._label_data)
a = self._combine_sparse_dense_features(batch_data["text_features"],
batch_data["text_mask"][0],
"text")
b = self._combine_sparse_dense_features(batch_data["label_features"],
batch_data["label_mask"][0],
"label")
all_bs = self._combine_sparse_dense_features(
label_data["label_features"], label_data["label_mask"][0], "label")
self.message_embed = self._create_tf_embed_fnn(
a,
self.hidden_layer_sizes["text"],
fnn_name="text_label" if self.share_hidden_layers else "text",
embed_name="text",
)
self.label_embed = self._create_tf_embed_fnn(
b,
self.hidden_layer_sizes["label"],
fnn_name="text_label" if self.share_hidden_layers else "label",
embed_name="label",
)
self.all_labels_embed = self._create_tf_embed_fnn(
all_bs,
self.hidden_layer_sizes["label"],
fnn_name="text_label" if self.share_hidden_layers else "label",
embed_name="label",
)
return train_utils.calculate_loss_acc(
self.message_embed,
self.label_embed,
b,
self.all_labels_embed,
all_bs,
self.num_neg,
None,
self.loss_type,
self.mu_pos,
self.mu_neg,
self.use_max_sim_neg,
self.C_emb,
self.scale_loss,
)
def predict_label(
self, message: "Message"
) -> Tuple[Dict[Text, Any], List[Dict[Text, Any]]]:
"""Predicts the intent of the provided message."""
label = {"name": None, "confidence": 0.0}
label_ranking = []
if self.session is None:
logger.error("There is no trained tf.session: "
"component is either not trained or "
"didn't receive enough training data.")
return label, label_ranking
# create session data from message and convert it into a batch of 1
session_data = self._create_session_data([message])
# if no text-features are present (e.g. incoming message is not in the
# vocab), do not predict a random intent
if not self._text_features_present(session_data):
return label, label_ranking
batch = train_utils.prepare_batch(session_data,
tuple_sizes=self.batch_tuple_sizes)
# load tf graph and session
label_ids, message_sim = self._calculate_message_sim(batch)
# if X contains all zeros do not predict some label
if label_ids.size > 0:
label = {
"name": self.inverted_label_dict[label_ids[0]],
"confidence": message_sim[0],
}
if self.ranking_length and 0 < self.ranking_length < LABEL_RANKING_LENGTH:
output_length = self.ranking_length
else:
output_length = LABEL_RANKING_LENGTH
ranking = list(zip(list(label_ids), message_sim))
ranking = ranking[:output_length]
label_ranking = [{
"name": self.inverted_label_dict[label_idx],
"confidence": score
} for label_idx, score in ranking]
return label, label_ranking