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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10
code/network.py
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@ -177,6 +177,13 @@ class AggregatingNeuralNetwork(NeuralNetwork):
count += 1
return total / float(count)
@staticmethod
def aggregate_max(weights):
max_found = weights[0]
for weight in weights:
max_found = weight > max_found and weight or max_found
return max_found
@staticmethod
def deaggregate_identically(aggregate, amount):
return [aggregate for _ in range(amount)]
@ -307,11 +314,12 @@ class RecurrentNeuralNetwork(NeuralNetwork):
return new_weights
if __name__ == '__main__':
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 = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False, use_bias=True)
# 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)