def cross(self, genome1, genome2, gene_tracker):
child_genome = Genome()
for node in genome1.nodes:
child_genome.add_node(node.copy())
for connection in genome1.connections:
new_connection = connection.copy()
if genome2.contains_connection(new_connection.in_node_id,
new_connection.out_node_id):
other_connection = genome2.get_connection(
new_connection.in_node_id, new_connection.out_node_id)
if not new_connection.enabled or not other_connection.enabled:
should_enable = random.random() > self.disable_probability
if new_connection.enabled != should_enable:
new_connection.disable(
) if new_connection.enabled else new_connection.enable(
)
if random.choice([True, False]):
new_connection.weight = other_connection.weight
child_genome.add_connection(new_connection)
return child_genome
def get_genome_from_standard_network(network: nn.Module,
key=1,
std=0.00001) -> Genome:
parameters = network.state_dict()
genome = Genome(key=key)
n_layers = len(parameters) // 2
nodes_per_layer = {}
# process nodes
node_key_index = 0
for i in range(n_layers):
biases = parameters[f'layer_{i}.bias'].numpy()
nodes_index = list(range(node_key_index, node_key_index + len(biases)))
nodes_per_layer[i] = nodes_index
node_key_index += len(nodes_index)
for bias, key in zip(biases, nodes_index):
genome.add_node(key=key, mean=bias, std=std)
# node_key_index += 1
# nodes_per_layer[i+1] = list(range(-1, -genome.n_input - 1, -1))
nodes_per_layer[i + 1] = list(range(-genome.n_input, 0))
# process connections
for i in range(n_layers):
weights = parameters[f'layer_{i}.weight'].numpy()
n_output, n_input = weights.shape
output_keys = nodes_per_layer[i]
# if nodes_per_layer.get(i+1):
input_keys = nodes_per_layer[i + 1]
for output_key, output_index in zip(output_keys, range(n_output)):
for input_key, input_index in zip(input_keys, range(n_input)):
key = (input_key, output_key)
genome.add_connection(key=key,
mean=weights[output_index][input_index],
std=std)
return genome
genome.add_node(Node(9, 1, ReLU(), 1.0)) genome.add_node(Node(10, 1, ReLU(), 1.0)) genome.add_node(Node(11, 2, ReLU(), 1.0)) genome.add_node(Node(12, 2, ReLU(), 1.0)) genome.add_node(Node(13, 2, ReLU(), 1.0)) genome.add_node(Node(14, 2, ReLU(), 1.0)) genome.add_node(Node(15, 3, ReLU(), 1.0)) genome.add_node(Node(16, 3, ReLU(), 1.0)) genome.add_node(Node(17, 3, ReLU(), 1.0)) genome.add_node(Node(4, 4, ReLU(), 1.0)) genome.add_node(Node(5, 4, ReLU(), 1.0)) genome.add_connection(Connection(1, 1, 6, True, 0, 0.5)) genome.add_connection(Connection(1, 1, 7, True, 0, -1.0)) genome.add_connection(Connection(1, 1, 9, True, 0, -0.4)) genome.add_connection(Connection(1, 2, 6, True, 0, 0.7)) genome.add_connection(Connection(1, 2, 7, True, 0, -0.1)) genome.add_connection(Connection(1, 3, 6, False, 0, 0.6)) genome.add_connection(Connection(1, 3, 9, True, 0, -0.18)) genome.add_connection(Connection(1, 3, 8, True, 0, 0.72)) genome.add_connection(Connection(1, 3, 7, True, 0, -0.9)) genome.add_connection(Connection(1, 6, 11, True, 0, 0.6)) genome.add_connection(Connection(1, 6, 13, False, 0, -0.5)) genome.add_connection(Connection(1, 7, 14, True, 0, 1.0)) genome.add_connection(Connection(1, 7, 12, True, 0, -0.9))