steffen/self-replicating-neural-networks
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added RecurrentNeuralNetwork, did some clean up

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Thomas Gabor
2019-03-03 03:37:49 +01:00
parent 865e3c4f36
commit c0d250e4f9
2 changed files with 86 additions and 42 deletions
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29
code/experiment.py
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@ -47,3 +47,32 @@ class Experiment:
for name,value in kwargs.items():
with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
dill.dump(value, dill_file)
class FixpointExperiment(Experiment):
def initialize_more(self):
self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
self.interesting_fixpoints = []
def run_net(self, net, step_limit=100):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
i += 1
self.count(net)
def count(self, net):
if net.is_diverged():
self.counters['divergent'] += 1
elif net.is_fixpoint():
if net.is_zero():
self.counters['fix_zero'] += 1
else:
self.counters['fix_other'] += 1
self.interesting_fixpoints.append(net)
self.log(net.repr_weights())
net.self_attack()
self.log(net.repr_weights())
elif net.is_fixpoint(2):
self.counters['fix_sec'] += 1
else:
self.counters['other'] += 1

89
code/other.py → code/network.py
View File

@ -8,7 +8,7 @@ from keras.layers import SimpleRNN, Dense
from keras.layers import Input, TimeDistributed
from tqdm import tqdm
from experiment import Experiment
from experiment import FixpointExperiment
def normalize_id(value, norm):
@ -35,6 +35,8 @@ def are_weights_within(network_weights, lower_bound, upper_bound):
return False
return True
class NeuralNetwork:
@staticmethod
@ -49,12 +51,10 @@ class NeuralNetwork:
s += "\n"
return s
def __init__(self, width, depth, **keras_params):
self.width = width
self.depth = depth
def __init__(self, **params):
self.params = dict(epsilon=0.00000000000001)
self.params.update(params)
self.keras_params = dict(activation='linear', use_bias=False)
self.keras_params.update(keras_params)
self.silent = True
def silence(self):
@ -127,7 +127,9 @@ class NeuralNetwork:
class WeightwiseNeuralNetwork(NeuralNetwork):
def __init__(self, width, depth, **kwargs):
super().__init__(width, depth, **kwargs)
super().__init__(**kwargs)
self.width = width
self.depth = depth
self.model = Sequential()
self.model.add(Dense(units=width, input_dim=4, **self.keras_params))
for _ in range(depth-1):
@ -156,7 +158,6 @@ class WeightwiseNeuralNetwork(NeuralNetwork):
return new_weights
class AggregatingNeuralNetwork(NeuralNetwork):
@staticmethod
@ -173,7 +174,9 @@ class AggregatingNeuralNetwork(NeuralNetwork):
return [aggregate for _ in range(amount)]
def __init__(self, aggregates, width, depth, **kwargs):
super().__init__(width, depth, **kwargs)
super().__init__(**kwargs)
self.width = width
self.depth = depth
self.aggregates = aggregates
self.aggregator = self.params.get('aggregator', self.__class__.aggregate_average)
self.deaggregator = self.params.get('deaggregator', self.__class__.deaggregate_identically)
@ -239,41 +242,53 @@ class AggregatingNeuralNetwork(NeuralNetwork):
return new_weights
class FixpointExperiment(Experiment):
def initialize_more(self):
self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
self.interesting_fixpoints = []
def run_net(self, net, step_limit=100):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
i += 1
self.count(net)
def count(self, net):
if net.is_diverged():
self.counters['divergent'] += 1
elif net.is_fixpoint():
if net.is_zero():
self.counters['fix_zero'] += 1
else:
self.counters['fix_other'] += 1
self.interesting_fixpoints.append(net)
self.log(net.repr_weights())
net.self_attack()
self.log(net.repr_weights())
elif net.is_fixpoint(2):
self.counters['fix_sec'] += 1
else:
self.counters['other'] += 1
class RecurrentNeuralNetwork(NeuralNetwork):
def __init__(self, width, depth, **kwargs):
super().__init__(**kwargs)
self.features = 1
self.width = width
self.depth = depth
self.model = Sequential()
self.model.add(SimpleRNN(units=width, input_dim=self.features, return_sequences=True, **self.keras_params))
for _ in range(depth-1):
self.model.add(SimpleRNN(units=width, return_sequences=True, **self.keras_params))
self.model.add(SimpleRNN(units=self.features, return_sequences=True, **self.keras_params))
def apply(self, *input):
stuff = np.transpose(np.array([[[input[i]] for i in range(len(input))]]))
return self.model.predict(stuff)[0].flatten()
def apply_to_weights(self, old_weights):
# build list from old weights
new_weights = copy.deepcopy(old_weights)
old_weights_list = []
for layer_id,layer in enumerate(old_weights):
for cell_id,cell in enumerate(layer):
for weight_id,weight in enumerate(cell):
old_weights_list += [weight]
# call network
new_weights_list = self.apply(*old_weights_list)
# write back new weights from list of rnn returns
current_weight_id = 0
for layer_id,layer in enumerate(new_weights):
for cell_id,cell in enumerate(layer):
for weight_id,weight in enumerate(cell):
new_weight = new_weights_list[current_weight_id]
new_weights[layer_id][cell_id][weight_id] = new_weight
current_weight_id += 1
return new_weights
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(print_all_weight_updates=False)
exp.run_net(net, 100)
# net = AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(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)