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Si11ium
2019-03-15 10:15:46 +01:00
parent 70e19dae1e
commit 3ca8dc15a3
29 changed files with 188 additions and 15 deletions
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26
code/soup.py
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@ -109,10 +109,11 @@ class Soup(object):
if __name__ == '__main__':
if True:
if False:
with SoupExperiment() as exp:
for run_id in range(1):
net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
# net_generator = lambda: FFTNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
@ -121,21 +122,26 @@ if __name__ == '__main__':
for _ in tqdm(range(1000)):
soup.evolve()
exp.log(soup.count())
exp.save(soup=soup.without_particles())
if False:
if True:
with SoupExperiment("soup") as exp:
for run_id in range(1):
net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)).with_keras_params(
activation='sigmoid').with_params(epsilon=0.0001)
# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2))\
.with_keras_params(activation='linear').with_params(epsilon=0.0001)
# net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))
# .with_keras_params(activation='linear')\
# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
# net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
# .with_keras_params(activation='linear')\
# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=10)
soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
soup.seed()
for _ in tqdm(range(100)):
soup.evolve()
soup.print_all()
exp.log(soup.count())
exp.save(soup=soup.without_particles()) # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
# or soup.historical_particles[particle_uid].states[time_step]['weights'] from soup.dill
# you can access soup.historical_particles[particle_uid].states[time_step]['loss']
# or soup.historical_particles[particle_uid].states[time_step]['weights']
# from soup.dill
exp.save(soup=soup.without_particles())