def random_grid_model(dim):
layer_grid = nx.create_empty_copy(nx.to_directed(nx.grid_2d_graph(dim[0], dim[1])))
for node in layer_grid.nodes.values():
if global_vars.get('simple_start'):
node['layer'] = IdentityLayer()
else:
node['layer'] = random_layer()
layer_grid.add_node('input')
layer_grid.nodes['input']['layer'] = IdentityLayer()
if global_vars.get('grid_as_ensemble'):
for row in range(dim[0]):
layer_grid.add_node(f'output_conv_{row}')
layer_grid.nodes[f'output_conv_{row}']['layer'] = IdentityLayer()
layer_grid.add_edge((row, dim[1] - 1), f'output_conv_{row}')
else:
layer_grid.add_node('output_conv')
layer_grid.nodes['output_conv']['layer'] = IdentityLayer()
layer_grid.add_edge((0, dim[1]-1), 'output_conv')
layer_grid.add_edge('input', (0, 0))
for i in range(dim[1]-1):
layer_grid.add_edge((0, i), (0, i+1))
layer_grid.graph['height'] = dim[0]
layer_grid.graph['width'] = dim[1]
if global_vars.get('parallel_paths_experiment'):
set_parallel_paths(layer_grid)
if check_legal_grid_model(layer_grid):
return layer_grid
else:
return random_grid_model(dim)
def mutate_models(individual, mutation_rate):
model = individual['model']
if random.random() < mutation_rate:
while True:
rand_layer = random.randint(0, len(model) - 1)
model[rand_layer] = random_layer()
if check_legal_model(model):
break
def mutate_layers_deap(individual):
while True:
rand_layer = random.randint(0, len(individual['model']) - 1)
prev_layer = individual['model'][rand_layer]
individual['model'][rand_layer] = random_layer()
if check_legal_model(individual['model']):
break
else:
individual[rand_layer] = prev_layer
return individual,
def mutate_modules_deap(individual):
rand_layer = random.randint(0, len(individual) - 1)
individual[rand_layer] = random_layer()
return individual,
def mutate_layer(model, layer_index):
old_layer = model[layer_index]
model[layer_index] = random_layer()
if not check_legal_model(model):
model[layer_index] = old_layer