def crossover(father, mother, alpha=0.5):
layers = []
for lt, ls, fl, ml in zip(father.get_layer_types(),
father.get_layer_shapes(), father.get_layers(),
mother.get_layers()):
if lt == "conv":
lyr = ConvLayer(image_shape=ls[0], filter_shape=ls[1])
# Loop for each filter
for i in range(ls[1][0]):
parent = ml
# Randomly pick either mother or father gene based on alpha
if random.random() < alpha:
parent = fl
lyr.set_filter(index=i, filtr=deepcopy(parent.get_filter(i)))
layers.append(lyr)
elif lt == "dense" or lt == "soft":
weights = []
biases = []
lyr = DenseLayer(layer_shape=ls[0])
if lt == "soft":
lyr = SoftmaxLayer(layer_shape=ls[0])
# Loop for each neuron
for i in range(ls[0][0]):
parent = ml
# Randomly pick either mother or father gene based on alpha
if random.random() < alpha:
parent = fl
weights.append(deepcopy(parent.get_weights(i)))
biases.append(deepcopy(parent.get_biases(i)))
lyr.set_weights_biases(weights, biases)
layers.append(lyr)
child = Individual(deepcopy(father.get_layer_types()),
deepcopy(father.get_layer_shapes()),
deepcopy(father.get_conv_layer_types()), layers)
child = mutate_individual(child)
return child
