def _apply_config(self, neural_net):
"""
This function actually does the work.
"""
if not isinstance(neural_net, NeuralNet):
raise ValueError("neural_net must be of the NeuralNet class.")
for snode in self.get_source_nodes(neural_net):
prev_copy_node = None
for level in xrange(self.copy_levels):
copy_node = CopyNode()
if level == 0:
copy_node.set_source_node(snode)
else:
copy_node.set_source_node(prev_copy_node)
copy_node.source_update_config(self.source_type,
self.incoming_weight,
self.existing_weight)
copy_node.set_activation_type(self.activation_type)
if self.connection_type == 'm':
self._fully_connect(copy_node,
self.get_upper_nodes(neural_net))
elif self.connection_type == 's':
copy_node.add_input_connection(Connection(
copy_node, snode))
else:
raise ValueError("Invalid connection_type")
neural_net.layers[self.copy_nodes_layer].add_node(copy_node)
prev_copy_node = copy_node
def _fully_connect(lower_node, upper_nodes):
"""
This function creates connections to each of the upper nodes.
This is a separate function from the one in layers, because using this
version does not require ALL of the nodes on a layer to be used.
"""
for upper_node in upper_nodes:
upper_node.add_input_connection(Connection(lower_node, upper_node))
