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Refactor:

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Step 4 - Aggregating Neural Networks
Step 5 - Training Neural Networks
This commit is contained in:
Si11ium 2019-06-14 09:55:51 +02:00
parent 9189759320
commit 4a81279b58
3 changed files with 101 additions and 86 deletions
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72
code/experiment.py
View File

@ -4,16 +4,22 @@ import dill
from tqdm import tqdm
import copy
from tensorflow.python.keras import backend as K
from abc import ABC, abstractmethod
class _BaseExperiment(ABC):
class Experiment(ABC):
@staticmethod
def from_dill(path):
with open(path, "rb") as dill_file:
return dill.load(dill_file)
@staticmethod
def reset_model():
K.clear_session()
def __init__(self, name=None, ident=None):
self.experiment_id = f'{ident or ""}_{time.time()}'
self.experiment_name = name or 'unnamed_experiment'
@ -59,22 +65,27 @@ class _BaseExperiment(ABC):
dill.dump(value, dill_file)
@abstractmethod
def run_net(self, network, iterations, run_id=0):
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
raise NotImplementedError
pass
def run_exp(self, network_generator, exp_iterations, prints=False, **kwargs):
# INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
for run_id in range(exp_iterations):
network = network_generator()
self.run_net(network, 100, run_id=run_id + 1, **kwargs)
self.historical_particles[run_id] = network
if prints:
print("Fixpoint? " + str(network.is_fixpoint()))
self.reset_model()
class Experiment(_BaseExperiment):
def __init__(self, **kwargs):
super(Experiment, self).__init__(**kwargs)
pass
def run_net(self, network, iterations, run_id=0):
pass
def reset_all(self):
self.reset_model()
class FixpointExperiment(Experiment):
if kwargs.get('logging', False):
self.log(self.counters)
def __init__(self, **kwargs):
kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
@ -82,7 +93,7 @@ class FixpointExperiment(Experiment):
self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
self.interesting_fixpoints = []
def run_net(self, net, step_limit=100, run_id=0):
def run_net(self, net, step_limit=100, run_id=0, **kwargs):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
@ -105,31 +116,56 @@ class FixpointExperiment(Experiment):
else:
self.counters['other'] += 1
def reset_counters(self):
for key in self.counters.keys():
self.counters[key] = 0
return True
def reset_all(self):
super(FixpointExperiment, self).reset_all()
self.reset_counters()
class MixedFixpointExperiment(FixpointExperiment):
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
for i in range(step_limit):
if net.is_diverged() or net.is_fixpoint():
break
net.self_attack()
with tqdm(postfix=["Loss", dict(value=0)]) as bar:
for _ in range(trains_per_application):
loss = net.compiled().train()
bar.postfix[1]["value"] = loss
bar.update()
i += 1
if run_id:
net.save_state()
self.count(net)
class SoupExperiment(Experiment):
pass
def __init__(self, **kwargs):
super(SoupExperiment, self).__init__(name=kwargs.get('name', self.__class__.__name__))
def run_exp(self, network_generator, exp_iterations, soup_generator=None, soup_iterations=0, prints=False):
for i in range(soup_iterations):
soup = soup_generator()
soup.seed()
for _ in tqdm(exp_iterations):
soup.evolve()
self.log(soup.count())
self.save(soup=soup.without_particles())
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
raise NotImplementedError
pass
class IdentLearningExperiment(Experiment):
def __init__(self):
super(IdentLearningExperiment, self).__init__(name=self.__class__.__name__)
pass
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
pass

57
code/network.py
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@ -529,57 +529,46 @@ class TrainingNeuralNetworkDecorator:
if __name__ == '__main__':
def run_exp(network, prints=False):
# INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
exp.run_net(network, 100, run_id=run_id + 1)
exp.historical_particles[run_id] = network
if prints:
print("Fixpoint? " + str(network.is_fixpoint()))
print("Loss " + str(loss))
if False:
if True:
# WeightWise Neural Network
net_generator = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear'))
with FixpointExperiment() as exp:
for run_id in tqdm(range(10)):
net = ParticleDecorator(
WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear'))
exp.run_exp(net)
K.clear_session()
exp.log(exp.counters)
exp.run_exp(net_generator, 10, logging=True)
exp.reset_all()
if False:
# Aggregating Neural Network
net_generator = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params())
with FixpointExperiment() as exp:
for run_id in tqdm(range(10)):
net = ParticleDecorator(
AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params())
run_exp(net)
K.clear_session()
exp.log(exp.counters)
exp.run_exp(net_generator, 10, logging=True)
exp.reset_all()
if False:
# FFT Aggregation
net_generator = lambda: ParticleDecorator(
AggregatingNeuralNetwork(
aggregates=4, width=2, depth=2, aggregator=AggregatingNeuralNetwork.aggregate_fft
).with_keras_params(activation='linear'))
with FixpointExperiment() as exp:
for run_id in tqdm(range(10)):
net = ParticleDecorator(
AggregatingNeuralNetwork(
aggregates=4, width=2, depth=2, aggregator=AggregatingNeuralNetwork.aggregate_fft
).with_keras_params(activation='linear'))
run_exp(net)
K.clear_session()
exp.log(exp.counters)
exp.run_exp(net_generator, 1)
exp.log(exp.counters)
exp.reset_model()
exp.reset_all()
if True:
# ok so this works quite realiably
with FixpointExperiment() as exp:
run_count = 1000
net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
net.with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
run_count = 10000
net_generator = TrainingNeuralNetworkDecorator(
ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2))
).with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
with MixedFixpointExperiment() as exp:
for run_id in tqdm(range(run_count+1)):
net.compiled()
loss = net.train(epoch=run_id)
exp.run_exp(net_generator, 1)
if run_id % 100 == 0:
run_exp(net)
exp.run_net(net_generator, 1)
K.clear_session()
if False:

58
code/soup.py
View File

@ -109,39 +109,29 @@ class Soup(object):
if __name__ == '__main__':
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()
soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
soup.seed()
for _ in tqdm(range(1000)):
soup.evolve()
exp.log(soup.count())
exp.save(soup=soup.without_particles())
if True:
net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
soup_generator = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
exp = SoupExperiment()
exp.run_exp(net_generator, 1000, soup_generator, 1, False)
# 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()
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')\
# .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(100, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
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())
net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
.with_keras_params(activation='linear').with_params(epsilon=0.0001)
soup_generator = lambda: Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
exp = SoupExperiment(name="soup")
exp.run_exp(net_generator, 100, soup_generator, 1, False)
# 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()