def backprop(layers, outputs=[], learning_rate=1):
for layer, neurons in reversed(list(enumerate(layers))):
is_input_layer = layer is 0
is_top_layer = layer is len(layers) - 1
if is_top_layer:
for index, neuron in enumerate(neurons):
s = neuron.output * (1 - neuron.output)
neuron.ideal_output = 1 if neuron.name in outputs else 0
neuron.error = (neuron.ideal_output - neuron.output) * s
for i, node in enumerate(layers[layer - 1]):
link = Select.link_between(node, neuron)
link.weight += ((neuron.error * node.output) * learning_rate)
link.save()
elif is_top_layer and not is_input_layer:
for index, neuron in enumerate(neurons):
error_sum = 0
s = neuron.output * (1 - output.neuron)
for i, node in enumerate(layers[layer + 1]):
link = Select.link_between(neuron, node)
error_sum += node.error * link.weight
neuron.error = error_sum * s
for i, node in enumerate(layers[layer - 1]):
link = Select.link_between(node, neuron)
link.weight += ((neuron.error * node.output) * learning_rate)
link.save()
return layers
def build_network(network):
layers = []
for layer in Select.layers_in(network):
neuron_list = []
for pool in Select.pools_in(layer):
for neuron in Select.neurons_in(pool):
neuron_list.append(neuron)
layers.append(neuron_list)
return layers
def activate_network(layers, inputs=[]):
for layer, neurons in enumerate(layers):
is_input_layer = layer is 0
for index, neuron in enumerate(neurons):
if is_input_layer:
neuron.output = 1 if neuron.name in inputs else 0
else:
weights = []
outputs = []
for i, node in enumerate(layers[layer - 1]):
outputs.append(node.output)
link = Select.link_between(node, neuron)
weights.append(link.weight)
weighted_sum = Computation.sum(outputs, weights)
neuron.output = Computation.activation(weighted_sum)
return layers
