steffen/self-replicating-neural-networks
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Box and stuff

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Si11ium
2019-03-13 15:30:54 +01:00
parent 5c7a646d69
commit d4f0024f4b
34 changed files with 413 additions and 211 deletions
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21
code/network.py
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@ -180,7 +180,7 @@ class ParticleDecorator:
def make_state(self, **kwargs):
weights = self.net.get_weights_flat()
if any(np.isinf(weights)):
if any(np.isinf(weights)) or any(np.isnan(weights)):
return None
state = {'class': self.net.__class__.__name__, 'weights': weights}
state.update(kwargs)
@ -609,8 +609,8 @@ class TrainingNeuralNetworkDecorator():
def train(self, batchsize=1, store_states=True, epoch=0):
self.compiled()
x, y = self.net.compute_samples()
savestatecallback = SaveStateCallback(net=self, epoch=epoch) if store_states else None
history = self.net.model.fit(x=x, y=y, verbose=0, batch_size=batchsize, callbacks=[savestatecallback] if store_states else None, initial_epoch=epoch)
savestatecallback = [SaveStateCallback(net=self, epoch=epoch)] if store_states else None
history = self.net.model.fit(x=x, y=y, epochs=epoch+1, verbose=0, batch_size=batchsize, callbacks=savestatecallback, initial_epoch=epoch)
return history.history['loss'][-1]
def learn_from(self, other_network, batchsize=1):
@ -628,36 +628,37 @@ if __name__ == '__main__':
exp.run_net(net, 100, run_id=run_id + 1)
exp.historical_particles[run_id] = net
if prints:
# print(net.apply_to_network(net))
print("Fixpoint? " + str(net.is_fixpoint()))
print("Loss " + str(loss))
K.clear_session()
if False:
if True:
# WeightWise Neural Network
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2) \
.with_keras_params(activation='linear'))
run_exp(net)
K.clear_session()
exp.log(exp.counters)
if False:
if True:
# Aggregating Neural Network
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
net = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2) \
.with_keras_params())
run_exp(net)
K.clear_session()
exp.log(exp.counters)
if False:
if True:
#FFT Neural Network
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
net = ParticleDecorator(FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
.with_keras_params(activation='linear'))
run_exp(net)
K.clear_session()
exp.log(exp.counters)
if True:
@ -665,13 +666,14 @@ if __name__ == '__main__':
with FixpointExperiment() as exp:
for i in range(1):
run_count = 1000
net = ParticleDecorator(TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
net.with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
for run_id in tqdm(range(run_count+1)):
net.compiled()
loss = net.train(epoch=run_id)
if run_id % 100 == 0:
run_exp(net)
K.clear_session()
if False:
with FixpointExperiment() as exp:
@ -689,6 +691,7 @@ if __name__ == '__main__':
print("Fixpoint after Agg? " + str(fp))
print("Loss " + str(loss))
print()
if False:
# this explodes in our faces completely... NAN everywhere
# TODO: Wtf is happening here?