31 lines
1.4 KiB
Python
31 lines
1.4 KiB
Python
import sys
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import os
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# Concat top Level dir to system environmental variables
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sys.path += os.path.join('..', '.')
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from soup import *
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from experiment import *
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if __name__ == '__main__':
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if True:
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with SoupExperiment("soup") as exp:
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for run_id in range(10):
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# net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
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# .with_keras_params(activation='linear').with_params(epsilon=0.0001)
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# net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
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# .with_keras_params(activation='linear')
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net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
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.with_keras_params(activation='linear')
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# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
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soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
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soup.seed()
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for _ in tqdm(range(100)):
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soup.evolve()
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exp.log(soup.count())
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# you can access soup.historical_particles[particle_uid].states[time_step]['loss']
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# or soup.historical_particles[particle_uid].states[time_step]['weights']
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# from soup.dill
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exp.save(soup=soup.without_particles())
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