33 lines
1.6 KiB
Python
33 lines
1.6 KiB
Python
import sys
|
|
import os
|
|
|
|
# Concat top Level dir to system environmental variables
|
|
sys.path += os.path.join('..', '.')
|
|
|
|
from soup import *
|
|
from experiment import *
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if True:
|
|
with SoupExperiment("soup") as exp:
|
|
for run_id in range(1):
|
|
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')
|
|
# net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
|
|
# .with_keras_params(activation='linear')
|
|
# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
|
|
soup = Soup(20, net_generator).with_params(remove_divergent=True, remove_zero=True,
|
|
train=30,
|
|
learn_from_rate=-1)
|
|
soup.seed()
|
|
for _ in tqdm(range(100)):
|
|
soup.evolve()
|
|
exp.log(soup.count())
|
|
# 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())
|