steffen/self-replicating-neural-networks
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model getter fixed

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Si11ium 2019-03-05 20:41:02 +01:00
parent de6aa68f23
commit 20e9545b02
11 changed files with 1961 additions and 1961 deletions
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12
code/network.py
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@ -575,7 +575,7 @@ class TrainingNeuralNetworkDecorator(NeuralNetwork):
return self return self
def get_model(self): def get_model(self):
return self.net.get_model() return self.net.model
def apply_to_weights(self, old_weights): def apply_to_weights(self, old_weights):
return self.net.apply_to_weights(old_weights) return self.net.apply_to_weights(old_weights)
@ -606,13 +606,13 @@ class TrainingNeuralNetworkDecorator(NeuralNetwork):
if __name__ == '__main__': if __name__ == '__main__':
if True: if False:
with FixpointExperiment() as exp: with FixpointExperiment() as exp:
for run_id in tqdm(range(100)): for run_id in tqdm(range(100)):
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear') # net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\ # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \ # net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
.with_params(print_all_weight_updates=False, use_bias=False) # .with_params(print_all_weight_updates=False, use_bias=False)
# net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')\ # net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')\
# .with_params(print_all_weight_updates=True) # .with_params(print_all_weight_updates=True)
# net.print_weights() # net.print_weights()
@ -636,10 +636,10 @@ if __name__ == '__main__':
net.print_weights() net.print_weights()
time.sleep(0.1) time.sleep(0.1)
print(net.is_fixpoint(epsilon=0.1e-6)) print(net.is_fixpoint(epsilon=0.1e-6))
if False: if True:
# ok so this works quite realiably # ok so this works quite realiably
with FixpointExperiment() as exp: with FixpointExperiment() as exp:
run_count = 1000 run_count = 100
net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\ net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\
.with_params(epsilon=0.0001).with_keras_params(optimizer='sgd') .with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
for run_id in tqdm(range(run_count+1)): for run_id in tqdm(range(run_count+1)):