def _build_tf_train_graph(self) -> Tuple["tf.Tensor", "tf.Tensor"]:
"""Bulid train graph using iterator."""
# iterator returns a_in, b_in, action_ids
self.a_in, self.b_in, _ = self._iterator.get_next()
if isinstance(self.featurizer, MaxHistoryTrackerFeaturizer):
# add time dimension if max history featurizer is used
self.b_in = self.b_in[:, tf.newaxis, :]
all_bot_raw = tf.constant(self._encoded_all_label_ids,
dtype=tf.float32,
name="all_bot_raw")
self.dial_embed, mask = self._create_tf_dial(self.a_in)
self.bot_embed = self._create_tf_bot_embed(self.b_in)
self.all_bot_embed = self._create_tf_bot_embed(all_bot_raw)
return train_utils.calculate_loss_acc(
self.dial_embed,
self.bot_embed,
self.b_in,
self.all_bot_embed,
all_bot_raw,
self.num_neg,
mask,
self.loss_type,
self.mu_pos,
self.mu_neg,
self.use_max_sim_neg,
self.C_emb,
self.scale_loss,
)
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 _build_tf_train_graph(self) -> Tuple["tf.Tensor", "tf.Tensor"]:
self.a_in, self.b_in = self._iterator.get_next()
all_label_ids = tf.constant(self._encoded_all_label_ids,
dtype=tf.float32,
name="all_label_ids")
self.message_embed = self._create_tf_embed_fnn(
self.a_in,
self.hidden_layer_sizes["a"],
fnn_name="a_b" if self.share_hidden_layers else "a",
embed_name="a",
)
self.label_embed = self._create_tf_embed_fnn(
self.b_in,
self.hidden_layer_sizes["b"],
fnn_name="a_b" if self.share_hidden_layers else "b",
embed_name="b",
)
self.all_labels_embed = self._create_tf_embed_fnn(
all_label_ids,
self.hidden_layer_sizes["b"],
fnn_name="a_b" if self.share_hidden_layers else "b",
embed_name="b",
)
return train_utils.calculate_loss_acc(
self.message_embed,
self.label_embed,
self.b_in,
self.all_labels_embed,
all_label_ids,
self.num_neg,
None,
self.loss_type,
self.mu_pos,
self.mu_neg,
self.use_max_sim_neg,
self.C_emb,
self.scale_loss,
)
