steffen/self-replicating-neural-networks
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tried out some stuff

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Thomas Gabor
2019-03-04 03:38:22 +01:00
parent 025d9cc337
commit 5295b51c28
3 changed files with 54 additions and 6 deletions
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18
code/soup.py
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@ -33,6 +33,11 @@ class Soup:
other_particle_id = int(prng() * len(self.particles))
other_particle = self.particles[other_particle_id]
particle.attack(other_particle)
if self.params.get('remove_divergent') and particle.is_diverged():
self.particles[particle_id] = self.generator()
if self.params.get('remove_zero') and particle.is_zero():
self.particles[particle_id] = self.generator()
def count(self):
counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
@ -53,9 +58,12 @@ class Soup:
if __name__ == '__main__':
with SoupExperiment() as exp:
for run_id in tqdm(range(1)):
net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
soup = Soup(100, net_generator)
for run_id in range(1):
net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='sigmoid').with_params()
# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
soup.seed()
soup.evolve(100)
exp.log(soup.count())
for _ in tqdm(range(100)):
soup.evolve()
exp.log(soup.count())