steffen/self-replicating-neural-networks
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first take on making soup

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This commit is contained in:
Thomas Gabor
2019-03-03 18:37:46 +01:00
parent dcfaa5ed4c
commit 025d9cc337
3 changed files with 82 additions and 10 deletions
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25
code/network.py
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@ -92,6 +92,14 @@ class NeuralNetwork:
self.attack(self)
return self
def meet(self, other_network):
new_other_network = copy.deepcopy(other_network)
return self.attack(new_other_network)
def self_meet(self, iterations=1):
new_me = copy.deepcopy(self)
return new_me.self_attack(iterations)
def is_diverged(self):
return are_weights_diverged(self.get_weights())
@ -300,13 +308,12 @@ class RecurrentNeuralNetwork(NeuralNetwork):
if __name__ == '__main__':
if True:
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
# net = WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear')
net = AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False)
# net = RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
# net.print_weights()
exp.run_net(net, 100)
exp.log(exp.counters)
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False)
# net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
# net.print_weights()
exp.run_net(net, 100)
exp.log(exp.counters)