def _build_tf_pred_graph(
self, session_data: "train_utils.SessionData") -> "tf.Tensor":
self.a_in = tf.placeholder(tf.float32,
(None, session_data.X.shape[-1]),
name="a")
self.b_in = tf.placeholder(tf.float32,
(None, None, session_data.Y.shape[-1]),
name="b")
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.sim_all = train_utils.tf_raw_sim(
self.message_embed[:, tf.newaxis, :],
self.all_labels_embed[tf.newaxis, :, :],
None,
)
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.sim = train_utils.tf_raw_sim(self.message_embed[:, tf.newaxis, :],
self.label_embed, None)
return train_utils.confidence_from_sim(self.sim_all,
self.similarity_type)
def _build_tf_pred_graph(self,
session_data: "SessionDataType") -> "tf.Tensor":
shapes, types = train_utils.get_shapes_types(session_data)
batch_placeholder = []
for s, t in zip(shapes, types):
batch_placeholder.append(tf.placeholder(t, s))
self.batch_in = tf.tuple(batch_placeholder)
batch_data, self.batch_tuple_sizes = train_utils.batch_to_session_data(
self.batch_in, session_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")
self.all_labels_embed = tf.constant(
self.session.run(self.all_labels_embed))
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.sim_all = train_utils.tf_raw_sim(
self.message_embed[:, tf.newaxis, :],
self.all_labels_embed[tf.newaxis, :, :],
None,
)
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.sim = train_utils.tf_raw_sim(self.message_embed[:, tf.newaxis, :],
self.label_embed, None)
return train_utils.confidence_from_sim(self.sim_all,
self.similarity_type)
def _build_tf_pred_graph(
self, session_data: "train_utils.SessionDataType") -> "tf.Tensor":
"""Rebuild tf graph for prediction."""
self._create_tf_placeholders(session_data)
self.dial_embed, mask = self._create_tf_dial(self.a_in)
self.sim_all = train_utils.tf_raw_sim(
self.dial_embed[:, :, tf.newaxis, :],
self.all_bot_embed[tf.newaxis, tf.newaxis, :, :],
mask,
)
self.bot_embed = self._create_tf_bot_embed(self.b_in)
self.sim = train_utils.tf_raw_sim(self.dial_embed[:, :, tf.newaxis, :],
self.bot_embed, mask)
return train_utils.confidence_from_sim(self.sim_all,
self.similarity_type)
