steffen/self-replicating-neural-networks
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Gelaber

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Si11ium 2019-03-14 18:36:29 +01:00
parent fd215be5de
commit 252716c420
5 changed files with 7 additions and 7 deletions
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2
code/setups/fixpoint-density.py
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@ -36,7 +36,7 @@ if __name__ == '__main__':
net_generators = []
for activation in ['linear', 'sigmoid', 'relu']:
net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
# net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
# net_generators += [lambda activation=activation: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
# net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
all_counters = []

6
code/setups/learn_from_soup.py
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@ -61,15 +61,15 @@ def count(counters, soup, notable_nets=[]):
with SoupExperiment('learn-from-soup') as exp:
exp.soup_size = 10
exp.soup_life = 1000
exp.trials = 20
exp.soup_life = 100
exp.trials = 10
exp.learn_from_severity_values = [10 * i for i in range(11)]
exp.epsilon = 1e-4
net_generators = []
for activation in ['sigmoid']: #['linear', 'sigmoid', 'relu']:
for use_bias in [False]:
net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
all_names = []

2
code/setups/mixed-self-fixpoints.py
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@ -61,7 +61,7 @@ if __name__ == '__main__':
for activation in ['linear']: # , 'sigmoid', 'relu']:
for use_bias in [False]:
net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]

2
code/setups/mixed-soup.py
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@ -54,7 +54,7 @@ def count(counters, soup, notable_nets=[]):
with Experiment('mixed-self-fixpoints') as exp:
exp.trials = 100
exp.trials = 10
exp.soup_size = 10
exp.soup_life = 5
exp.trains_per_selfattack_values = [10 * i for i in range(11)]

2
code/setups/training-fixpoints.py
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@ -40,7 +40,7 @@ if __name__ == '__main__':
for activation in ['linear']: # , 'sigmoid', 'relu']:
for use_bias in [False]:
net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
# net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
all_counters = []
all_notable_nets = []