steffen/self-replicating-neural-networks
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bar plots

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Si11ium
2019-03-15 18:59:50 +01:00
parent 809f178e2f
commit 869833da3b
39 changed files with 275 additions and 120 deletions
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33
code/setups/network_trajectorys.py
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@@ -19,19 +19,18 @@ if __name__ == '__main__':
if True:
# WeightWise Neural Network
for _ in range(10):
with FixpointExperiment() as exp:
for run_id in tqdm(range(20)):
net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
.with_keras_params(activation='linear'))
run_exp(net)
K.clear_session()
exp.log(exp.counters)
exp.save(trajectorys=exp.without_particles())
with FixpointExperiment(name="weightwise_self_application") as exp:
for run_id in tqdm(range(20)):
net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
.with_keras_params(activation='linear'))
run_exp(net)
K.clear_session()
exp.log(exp.counters)
exp.save(trajectorys=exp.without_particles())
if False:
# Aggregating Neural Network
with FixpointExperiment() as exp:
with FixpointExperiment(name="aggregating_self_application") as exp:
for run_id in tqdm(range(10)):
net = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
.with_keras_params(activation='linear'))
@@ -53,31 +52,33 @@ if __name__ == '__main__':
if False:
# ok so this works quite realiably
with FixpointExperiment() as exp:
with FixpointExperiment(name="weightwise_learning") as exp:
for i in range(10):
run_count = 100
net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
exp.historical_particles[net.get_uid()] = net
for run_id in tqdm(range(run_count+1)):
net.compiled()
loss = net.train(epoch=run_id)
if run_id % 10 == 0:
run_exp(net)
# run_exp(net)
# net.save_state(time=run_id)
K.clear_session()
exp.save(trajectorys=exp.without_particles())
if False:
# ok so this works quite realiably
with FixpointExperiment() as exp:
with FixpointExperiment(name="aggregating_learning") as exp:
for i in range(10):
run_count = 100
net = TrainingNeuralNetworkDecorator(ParticleDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)))
net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
exp.historical_particles[net.get_uid()] = net
for run_id in tqdm(range(run_count+1)):
net.compiled()
loss = net.train(epoch=run_id)
if run_id % 10 == 0:
run_exp(net)
# run_exp(net)
# net.save_state(time=run_id)
K.clear_session()
exp.save(trajectorys=exp.without_particles())