steffen/self-replicating-neural-networks
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Si11ium
2019-03-05 19:50:39 +01:00
parent 1639d6c0f1
commit 6625481091
3 changed files with 182 additions and 92 deletions
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26
code/network.py
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@ -136,11 +136,11 @@ class NeuralNetwork(PrintingObject):
return False
return True
def repr_weights(self):
return self.__class__.weights_to_string(self.get_weights())
def repr_weights(self, weights=None):
return self.weights_to_string(weights or self.get_weights())
def print_weights(self):
print(self.repr_weights())
def print_weights(self, weights=None):
print(self.repr_weights(weights))
class WeightwiseNeuralNetwork(NeuralNetwork):
@ -605,7 +605,7 @@ class TrainingNeuralNetworkDecorator(NeuralNetwork):
if __name__ == '__main__':
if False:
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
for run_id in tqdm(range(1)):
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
@ -613,9 +613,10 @@ if __name__ == '__main__':
# 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)
# INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
exp.run_net(net, 100, run_id=run_id+1)
exp.log(exp.counters)
if False:
# is_fixpoint was wrong because it trivially returned the old weights
with IdentLearningExperiment() as exp:
@ -679,15 +680,16 @@ if __name__ == '__main__':
print("Fixpoint? " + str(net.is_fixpoint()))
print("Loss " + str(loss))
print()
if True:
if False:
# and this gets somewhat interesting... we can still achieve non-trivial fixpoints
# over multiple applications when training enough in-between
with MixedFixpointExperiment() as exp:
for run_id in range(100):
net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\
.with_params(epsilon=0.0001)
for run_id in range(10):
net = TrainingNeuralNetworkDecorator(FFTNeuralNetwork(2, width=2, depth=2))\
.with_params(epsilon=0.0001, activation='sigmoid')
exp.run_net(net, 500, 10)
net.print_weights()
print("Fixpoint? " + str(net.is_fixpoint()))
print()
exp.log(exp.counters)