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 add_nodes(self, number_nodes, node_type, activation_type=None):
"""
This function adds nodes in bulk for initialization.
If an optional activation type is passed through, that will be set for
the nodes. Otherwise, the default activation type for the layer will
be used.
"""
count = 0
while count < number_nodes:
if node_type == 'copy':
node = CopyNode()
else:
node = Node(node_type)
if activation_type:
node.set_activation_type(activation_type)
self.add_node(node)
count += 1
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
