fixed some stuff in the code
This commit is contained in:
Thomas Gabor
1404
code/network.py
1404
code/network.py
File diff suppressed because it is too large
Load Diff
212
code/soup.py
212
code/soup.py
@ -1,104 +1,108 @@
|
||||
import random
|
||||
import copy
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
from experiment import *
|
||||
from network import *
|
||||
|
||||
|
||||
def prng():
|
||||
return random.random()
|
||||
|
||||
|
||||
class Soup:
|
||||
|
||||
def __init__(self, size, generator, **kwargs):
|
||||
self.size = size
|
||||
self.generator = generator
|
||||
self.particles = []
|
||||
self.params = dict(meeting_rate=0.1, train_other_rate=0.1, train=0)
|
||||
self.params.update(kwargs)
|
||||
|
||||
def with_params(self, **kwargs):
|
||||
self.params.update(kwargs)
|
||||
return self
|
||||
|
||||
def seed(self):
|
||||
self.particles = []
|
||||
for _ in range(self.size):
|
||||
self.particles += [self.generator()]
|
||||
return self
|
||||
|
||||
def evolve(self, iterations=1):
|
||||
for _ in range(iterations):
|
||||
for particle_id, particle in enumerate(self.particles):
|
||||
if prng() < self.params.get('meeting_rate'):
|
||||
other_particle_id = int(prng() * len(self.particles))
|
||||
other_particle = self.particles[other_particle_id]
|
||||
particle.attack(other_particle)
|
||||
if prng() < self.params.get('train_other_rate'):
|
||||
other_particle_id = int(prng() * len(self.particles))
|
||||
other_particle = self.particles[other_particle_id]
|
||||
particle.train_other(other_particle)
|
||||
try:
|
||||
for _ in range(self.params.get('train', 0)):
|
||||
particle.compiled().train()
|
||||
except AttributeError:
|
||||
pass
|
||||
if self.params.get('remove_divergent') and particle.is_diverged():
|
||||
self.particles[particle_id] = self.generator()
|
||||
if self.params.get('remove_zero') and particle.is_zero():
|
||||
self.particles[particle_id] = self.generator()
|
||||
|
||||
def count(self):
|
||||
counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
|
||||
for particle in self.particles:
|
||||
if particle.is_diverged():
|
||||
counters['divergent'] += 1
|
||||
elif particle.is_fixpoint():
|
||||
if particle.is_zero():
|
||||
counters['fix_zero'] += 1
|
||||
else:
|
||||
counters['fix_other'] += 1
|
||||
elif particle.is_fixpoint(2):
|
||||
counters['fix_sec'] += 1
|
||||
else:
|
||||
counters['other'] += 1
|
||||
return counters
|
||||
|
||||
|
||||
class LearningSoup(Soup):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(LearningSoup, self).__init__(**kwargs)
|
||||
|
||||
|
||||
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: 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(100)):
|
||||
soup.evolve()
|
||||
exp.log(soup.count())
|
||||
|
||||
if True:
|
||||
with SoupExperiment() as exp:
|
||||
for run_id in range(1):
|
||||
net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)).with_keras_params(
|
||||
activation='linear')
|
||||
# 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(10, net_generator).with_params(remove_divergent=True, remove_zero=True).with_params(train=500)
|
||||
soup.seed()
|
||||
for _ in tqdm(range(10)):
|
||||
soup.evolve()
|
||||
exp.log(soup.count())
|
||||
|
||||
import random
|
||||
import copy
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
from experiment import *
|
||||
from network import *
|
||||
|
||||
|
||||
def prng():
|
||||
return random.random()
|
||||
|
||||
|
||||
class Soup:
|
||||
|
||||
def __init__(self, size, generator, **kwargs):
|
||||
self.size = size
|
||||
self.generator = generator
|
||||
self.particles = []
|
||||
self.params = dict(meeting_rate=0.1, train_other_rate=0.1, train=0)
|
||||
self.params.update(kwargs)
|
||||
|
||||
def with_params(self, **kwargs):
|
||||
self.params.update(kwargs)
|
||||
return self
|
||||
|
||||
def seed(self):
|
||||
self.particles = []
|
||||
for _ in range(self.size):
|
||||
self.particles += [self.generator()]
|
||||
return self
|
||||
|
||||
def evolve(self, iterations=1):
|
||||
for _ in range(iterations):
|
||||
for particle_id, particle in enumerate(self.particles):
|
||||
if prng() < self.params.get('meeting_rate'):
|
||||
other_particle_id = int(prng() * len(self.particles))
|
||||
other_particle = self.particles[other_particle_id]
|
||||
particle.attack(other_particle)
|
||||
if prng() < self.params.get('train_other_rate'):
|
||||
other_particle_id = int(prng() * len(self.particles))
|
||||
other_particle = self.particles[other_particle_id]
|
||||
particle.train_other(other_particle)
|
||||
for _ in range(self.params.get('train', 0)):
|
||||
particle.compiled().train()
|
||||
if self.params.get('remove_divergent') and particle.is_diverged():
|
||||
self.particles[particle_id] = self.generator()
|
||||
if self.params.get('remove_zero') and particle.is_zero():
|
||||
self.particles[particle_id] = self.generator()
|
||||
|
||||
def count(self):
|
||||
counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
|
||||
for particle in self.particles:
|
||||
if particle.is_diverged():
|
||||
counters['divergent'] += 1
|
||||
elif particle.is_fixpoint():
|
||||
if particle.is_zero():
|
||||
counters['fix_zero'] += 1
|
||||
else:
|
||||
counters['fix_other'] += 1
|
||||
elif particle.is_fixpoint(2):
|
||||
counters['fix_sec'] += 1
|
||||
else:
|
||||
counters['other'] += 1
|
||||
return counters
|
||||
|
||||
def print_all(self):
|
||||
for particle in self.particles:
|
||||
particle.print_weights()
|
||||
print(particle.is_fixpoint())
|
||||
|
||||
|
||||
class LearningSoup(Soup):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(LearningSoup, self).__init__(**kwargs)
|
||||
|
||||
|
||||
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: 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(100)):
|
||||
soup.evolve()
|
||||
exp.log(soup.count())
|
||||
|
||||
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')\
|
||||
# .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=200)
|
||||
soup.seed()
|
||||
for _ in tqdm(range(10)):
|
||||
soup.evolve()
|
||||
soup.print_all()
|
||||
exp.log(soup.count())
|
||||
|
||||
|
||||
Reference in New Issue
Block a user