output = net.activate(inputs)
error += (output[0] - s) ** 2
return -(error / (N * num_tests)) ** 0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
print('\nBest genome:')
winner = pop.statistics.best_genome()
print(winner)
def test_concurrent_nn():
"""This is a stripped-down copy of the `memory` example."""
# num_tests is the number of random examples each network is tested against.
num_tests = 16
# N is the length of the test sequence.
N = 4
def eval_fitness(genomes):
for g in genomes:
net = nn.create_recurrent_phenotype(g)
error = 0.0
for _ in range(num_tests):
# Create a random sequence, and feed it to the network with the
# second input set to zero.
seq = [random.choice((0, 1)) for _ in range(N)]
net.reset()
for s in seq:
inputs = [s, 0]
net.activate(inputs)
# Set the second input to one, and get the network output.
for s in seq:
inputs = [0, 1]
output = net.activate(inputs)
error += (output[0] - s)**2
g.fitness = -(error / (N * num_tests))**0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'recurrent_config'))
pop.run(eval_fitness, 10)
# Visualize the winner network and plot/log statistics.
# visualize.draw_net(winner, view=True, filename="nn_winner.gv")
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
# visualize.plot_stats(pop.statistics)
# visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
repr(winner)
str(winner)
for g in winner.node_genes:
repr(g)
str(g)
for g in winner.conn_genes:
repr(g)
str(g)
def test_concurrent_nn():
"""This is a stripped-down copy of the `memory` example."""
# num_tests is the number of random examples each network is tested against.
num_tests = 16
# N is the length of the test sequence.
N = 4
def eval_fitness(genomes):
for g in genomes:
net = nn.create_recurrent_phenotype(g)
error = 0.0
for _ in range(num_tests):
# Create a random sequence, and feed it to the network with the
# second input set to zero.
seq = [random.choice((0, 1)) for _ in range(N)]
net.reset()
for s in seq:
inputs = [s, 0]
net.activate(inputs)
# Set the second input to one, and get the network output.
for s in seq:
inputs = [0, 1]
output = net.activate(inputs)
error += (output[0] - s) ** 2
g.fitness = -(error / (N * num_tests)) ** 0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'recurrent_config'))
pop.run(eval_fitness, 10)
# Visualize the winner network and plot/log statistics.
# visualize.draw_net(winner, view=True, filename="nn_winner.gv")
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
# visualize.plot_stats(pop.statistics)
# visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
repr(winner)
str(winner)
for g in winner.node_genes:
repr(g)
str(g)
for g in winner.conn_genes:
repr(g)
str(g)
output = net.activate(inputs)
error += (output[0] - s) ** 2
return -(error / (N * num_tests)) ** 0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
print('\nBest genome:')
winner = pop.statistics.best_genome()
print(winner)
error += (output[0] - s) ** 2
return -(error / (N * num_tests)) ** 0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add("my_sinc_function", sinc)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, "nn_config"))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print("Number of evaluations: {0}".format(pop.total_evaluations))
evolve()
c += 1
for i, g in enumerate(group):
genomes[g].fitness += countSpecies(gameOfLife.world['space'],
i)
#results of fights define the fitness
for g in genomes:
g.fitness = g.fitness / num_runs
#adding elu to activation functions
def elu(x):
return x if x < 0 else np.exp(x) - 1
activation_functions.add('elu', elu)
def main():
print("Starting...")
pop = population.Population(
os.path.join(os.path.dirname(__file__), 'nn_config'))
#HINT change checkpoints for new try or reloading
pop.load_checkpoint(
os.path.join(os.path.dirname(__file__), 'checkpoints/popv1.cpt'))
pop.run(eval_fitness_internalfight, 5)
pop.save_checkpoint(
os.path.join(os.path.dirname(__file__), 'checkpoints/popv1.cpt'))
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
# divide training results by traincount, bc of padding etc this has to be done
# fitness of all below median is set to zero
trainfitness = trainfitness/traincount
trainfitness[trainfitness < np.median(trainfitness)] = 0
# results of fights define the fitness
for k, g in enumerate(allgenomes):
g.fitness = trainfitness[k]
# adding elu to activation functions
def elu(x):
return x if x < 0 else np.exp(x) - 1
activation_functions.add('elu', elu)
def train(checkpoint, config):
print("Starting...")
pop = population.Population(config)
# HINT change checkpoints for new try or reloading
try:
pop.load_checkpoint(checkpoint)
except:
print("Checkpoint not found, starting from scratch: ", checkpoint)
trainfunc = partial(eval_fitness_internalfight, num_runs=4, steplength=100, x=16, y=16)
pop.run(trainfunc, 20)
pop.save_checkpoint(checkpoint)
statistics.save_stats(pop.statistics)
