steffen/self-replicating-neural-networks
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added shuffling to AggregatingNeuralNetwork in a futile attempt to reproduce former results

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Thomas Gabor
2019-03-03 04:03:31 +01:00
parent c0d250e4f9
commit dcfaa5ed4c
1 changed files with 26 additions and 8 deletions
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34
code/network.py
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@@ -173,19 +173,36 @@ class AggregatingNeuralNetwork(NeuralNetwork):
def deaggregate_identically(aggregate, amount): def deaggregate_identically(aggregate, amount):
return [aggregate for _ in range(amount)] return [aggregate for _ in range(amount)]
@staticmethod
def shuffle_not(weights_list):
return weights_list
@staticmethod
def shuffle_random(weights_list):
import random
random.shuffle(weights_list)
return weights_list
def __init__(self, aggregates, width, depth, **kwargs): def __init__(self, aggregates, width, depth, **kwargs):
super().__init__(**kwargs) super().__init__(**kwargs)
self.aggregates = aggregates
self.width = width self.width = width
self.depth = depth 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)
self.model = Sequential() self.model = Sequential()
self.model.add(Dense(units=width, input_dim=self.aggregates, **self.keras_params)) self.model.add(Dense(units=width, input_dim=self.aggregates, **self.keras_params))
for _ in range(depth-1): for _ in range(depth-1):
self.model.add(Dense(units=width, **self.keras_params)) self.model.add(Dense(units=width, **self.keras_params))
self.model.add(Dense(units=self.aggregates, **self.keras_params)) self.model.add(Dense(units=self.aggregates, **self.keras_params))
def get_aggregator(self):
return self.params.get('aggregator', self.__class__.aggregate_average)
def get_deaggregator(self):
return self.params.get('deaggregator', self.__class__.deaggregate_identically)
def get_shuffler(self):
return self.params.get('shuffler', self.__class__.shuffle_not)
def get_amount_of_weights(self): def get_amount_of_weights(self):
total_weights = 0 total_weights = 0
for layer_id,layer in enumerate(self.get_weights()): for layer_id,layer in enumerate(self.get_weights()):
@@ -215,15 +232,16 @@ class AggregatingNeuralNetwork(NeuralNetwork):
collections[-1] += next_collection collections[-1] += next_collection
leftovers = len(next_collection) leftovers = len(next_collection)
# call network # call network
old_aggregations = [self.aggregator(collection) for collection in collections] old_aggregations = [self.get_aggregator()(collection) for collection in collections]
new_aggregations = self.apply(*old_aggregations) new_aggregations = self.apply(*old_aggregations)
# generate list of new weights # generate list of new weights
new_weights_list = [] new_weights_list = []
for aggregation_id,aggregation in enumerate(new_aggregations): for aggregation_id,aggregation in enumerate(new_aggregations):
if aggregation_id == self.aggregates - 1: if aggregation_id == self.aggregates - 1:
new_weights_list += self.deaggregator(aggregation, collection_size + leftovers) new_weights_list += self.get_deaggregator()(aggregation, collection_size + leftovers)
else: else:
new_weights_list += self.deaggregator(aggregation, collection_size) new_weights_list += self.get_deaggregator()(aggregation, collection_size)
new_weights_list = self.get_shuffler()(new_weights_list)
# write back new weights # write back new weights
new_weights = copy.deepcopy(old_weights) new_weights = copy.deepcopy(old_weights)
current_weight_id = 0 current_weight_id = 0
@@ -286,8 +304,8 @@ if __name__ == '__main__':
with FixpointExperiment() as exp: with FixpointExperiment() as exp:
for run_id in tqdm(range(100)): for run_id in tqdm(range(100)):
# net = WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear') # 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) 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 = RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
# net.print_weights() # net.print_weights()
exp.run_net(net, 100) exp.run_net(net, 100)
exp.log(exp.counters) exp.log(exp.counters)