rasa/core/policies/embedding_policy.py
def _create_tf_no_action_embed(self,
                                   y_for_no_action_in: tf.Tensor) -> tf.Tensor:
        """Create embedding bot vector for empty action and action_listen."""

        layer_name_suffix = 'a_and_b' if self.share_embedding else 'b'

        y_for_no_action = self._create_tf_nn(
            y_for_no_action_in,
            self.hidden_layer_sizes['b'],
            droprate=0,
            layer_name_suffix=layer_name_suffix
        )
        return tf.stop_gradient(
            self._create_embed(y_for_no_action,
                               layer_name_suffix=layer_name_suffix))
Create embedding bot vector for empty action and action_listen.
Create embedding bot vector for empty action and action_listen.
Create embedding bot vector for empty action and action_listen .
def _create_tf_no_action_embed(self,
                                   y_for_no_action_in: tf.Tensor) -> tf.Tensor:
        layer_name_suffix = 'a_and_b' if self.share_embedding else 'b'
        y_for_no_action = self._create_tf_nn(
            y_for_no_action_in,
            self.hidden_layer_sizes['b'],
            droprate=0,
            layer_name_suffix=layer_name_suffix
        )
        return tf.stop_gradient(
            self._create_embed(y_for_no_action,
                               layer_name_suffix=layer_name_suffix))
def _create_tf_no_action_embed ( self , y_for_no_action_in : tf . Tensor ) -> tf . Tensor : layer_name_suffix = 'a_and_b' if self . share_embedding else 'b' y_for_no_action = self . _create_tf_nn ( y_for_no_action_in , self . hidden_layer_sizes [ 'b' ] , droprate = 0 , layer_name_suffix = layer_name_suffix ) return tf . stop_gradient ( self . _create_embed ( y_for_no_action , layer_name_suffix = layer_name_suffix ) )
Create embedding bot vector for empty action and action_listen.
Create embedding bot vector for empty action and action_listen .
"""Create embedding bot vector for empty action and action_listen."""
self
y_for_no_action_in
tf.Tensor