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_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)