def build_feedforward_multilayered(input_number = 2, hidden_numbers = [2], output_number = 1, hidden_function = "tanh", output_function = "logsig"):
net = network()
net.num_of_inputs = input_number
inputs = range(input_number)
biasnode = net.bias_node
#Hidden layers
prev_layer = inputs
for hidden_number in hidden_numbers:
current_layer = []
for i in xrange(int(hidden_number)):
hidden = node(hidden_function)
connect_node(hidden, biasnode)
connect_nodes(hidden, prev_layer)
net.hidden_nodes.append(hidden)
current_layer.append(hidden)
prev_layer = current_layer
#Output nodes
for i in xrange(int(output_number)):
output = node(output_function)
connect_node(output, biasnode)
connect_nodes(output, prev_layer)
net.output_nodes.append(output)
return net
population.append(network())
population[child_index].num_of_inputs = mother.num_of_inputs
bias_child = population[child_index].bias_node
#Hidden layer
for mother_node, father_node in zip(mother.hidden_nodes, father.hidden_nodes):
hidden = node(mother_node.activation_function, random_range)
weights = {}
for input_number in xrange(mother.num_of_inputs):
choice = sample([mother_node, father_node], 1)[0]
weights[input_number] = choice.weights[input_number]
choice = sample([(mother, mother_node), (father, father_node)], 1)[0]
weights[bias_child] = choice[1].weights[choice[0].bias_node]
_all = range(mother.num_of_inputs)
_all.append(bias_child)
connect_nodes(hidden, _all, weights)
population[child_index].hidden_nodes.append(hidden)
hidden_nodes = population[child_index].hidden_nodes
#Output nodes
#for mother_node, father_node in zip(mother.output_nodes, father.output_nodes):
for output_number in xrange(len(mother.output_nodes)):
output = node(mother.output_nodes[output_number].activation_function)
weights = {}
for hidden_number in xrange(len(mother.hidden_nodes)):
choice = sample([mother, father], 1)[0]
weights[hidden_nodes[hidden_number]] = choice.output_nodes[output_number].weights[choice.hidden_nodes[hidden_number]]
choice = sample([mother, father], 1)[0]
weights[bias_child] = choice.output_nodes[output_number].weights[choice.bias_node]