def embed_inputs(self, inputs: tf.Tensor) -> tf.Tensor:
embedded = tf.nn.embedding_lookup(self.embedding_matrix, inputs)
if (self.embeddings_source is not None
and self.embeddings_source.scale_embeddings_by_depth):
# Pylint @property-related bug
# pylint: disable=no-member
embedding_size = self.embedding_matrix.shape.as_list()[-1]
# pylint: enable=no-member
embedded *= math.sqrt(embedding_size)
length = tf.shape(inputs)[1]
return embedded + position_signal(self.dimension, length)
def embed_input_symbol(self, inputs: tf.Tensor) -> tf.Tensor:
embedded = tf.nn.embedding_lookup(self.embedding_matrix, inputs)
if (self.embeddings_source is not None
and self.embeddings_source.scale_embeddings_by_depth):
# Pylint @property-related bug
# pylint: disable=no-member
embedding_size = self.embedding_matrix.shape.as_list()[-1]
# pylint: enable=no-member
embedded *= math.sqrt(embedding_size)
length = tf.shape(inputs)[1]
return dropout(embedded + position_signal(self.dimension, length),
self.dropout_keep_prob,
self.train_mode)
def embed_inputs(self, inputs: tf.Tensor) -> tf.Tensor:
embedded = tf.nn.embedding_lookup(self.embedding_matrix, inputs)
length = tf.shape(inputs)[1]
return embedded + position_signal(self.dimension, length)