steffen/self-replicating-neural-networks
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Si11ium
2019-03-07 20:07:37 +01:00
parent 95c2ff4200
commit 7a76b1ba88
4 changed files with 35 additions and 41 deletions
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12
code/experiment.py
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@ -18,13 +18,12 @@ class Experiment:
self.base_dir = self.experiment_name
self.next_iteration = 0
self.log_messages = []
self.historical_particles = dict()
self.historical_particles = {}
def __enter__(self):
self.dir = os.path.join(self.base_dir, 'experiments', 'exp-{name}-{id}-{it}'.format(
self.dir = os.path.join('experiments', 'exp-{name}-{id}-{it}'.format(
name=self.experiment_name, id=self.experiment_id, it=self.next_iteration)
)
os.makedirs(self.dir)
print("** created {dir} **".format(dir=self.dir))
return self
@ -60,10 +59,6 @@ class Experiment:
with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
dill.dump(value, dill_file)
def add_trajectory_segment(self, run_id, trajectory):
self.historical_particles[run_id].append(trajectory)
return
class FixpointExperiment(Experiment):
@ -78,8 +73,7 @@ class FixpointExperiment(Experiment):
net.self_attack()
i += 1
if run_id:
weights = net.get_weights_flat()
self.add_trajectory_segment(run_id, weights)
net.save_state(time=run_id)
self.count(net)
def count(self, net):

37
code/network.py
View File

@ -1,5 +1,3 @@
import math
import copy
import numpy as np
from keras.models import Sequential
@ -47,9 +45,9 @@ class NeuralNetwork(PrintingObject):
for layer_id, layer in enumerate(network_weights):
for cell_id, cell in enumerate(layer):
for weight_id, weight in enumerate(cell):
if math.isnan(weight):
if np.isnan(weight):
return True
if math.isinf(weight):
if np.isinf(weight):
return True
return False
@ -100,13 +98,13 @@ class NeuralNetwork(PrintingObject):
return self
def get_weights(self):
return self.get_model().get_weights()
return self.model.get_weights()
def get_weights_flat(self):
return np.hstack([weight.flatten() for weight in self.get_weights()])
def set_weights(self, new_weights):
return self.get_model().set_weights(new_weights)
return self.model.set_weights(new_weights)
def apply_to_weights(self, old_weights):
raise NotImplementedError
@ -133,11 +131,11 @@ class NeuralNetwork(PrintingObject):
return self.attack(new_other_network)
def is_diverged(self):
return NeuralNetwork.are_weights_diverged(self.get_weights())
return self.are_weights_diverged(self.get_weights())
def is_zero(self, epsilon=None):
epsilon = epsilon or self.get_params().get('epsilon')
return NeuralNetwork.are_weights_within(self.get_weights(), -epsilon, epsilon)
return self.are_weights_within(self.get_weights(), -epsilon, epsilon)
def is_fixpoint(self, degree=1, epsilon=None):
assert degree >= 1, "degree must be >= 1"
@ -167,7 +165,7 @@ class NeuralNetwork(PrintingObject):
def make_state(self, **kwargs):
weights = self.get_weights_flat()
state = {'class': self.__class__.__name__, 'weights': weights}
if any(np.isinf(weights)):
if any(np.isnan(weights)) or any(np.isinf(weights)):
return None
state.update(kwargs)
return state
@ -593,7 +591,8 @@ class LearningNeuralNetwork(NeuralNetwork):
for epoch in range(epochs):
old_weights = self.get_weights()
x = reduction(old_weights, self.features)
history = self.model.fit(x=x, y=x, verbose=0, batch_size=batchsize)
savestateCallback = SaveStateCallback(self, epoch=epoch)
history = self.model.fit(x=x, y=x, verbose=0, batch_size=batchsize, callbacks=savestateCallback)
bar.postfix[1]["value"] = history.history['loss'][-1]
bar.update()
@ -654,18 +653,20 @@ if __name__ == '__main__':
if False:
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
# net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
# .with_params(print_all_weight_updates=False, use_bias=False)
net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
.with_params(print_all_weight_updates=False, use_bias=False)
# net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')\
# .with_params(print_all_weight_updates=True)
# net.print_weights()
# INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
exp.run_net(net, 100, run_id=run_id+1)
exp.historical_particles[run_id] = net
exp.log(exp.counters)
if False:
# TODO SI: Ich muss noch apply to weights implementieren
# is_fixpoint was wrong because it trivially returned the old weights
with IdentLearningExperiment() as exp:
net = LearningNeuralNetwork(width=2, depth=2, features=2, )\
@ -674,17 +675,13 @@ if __name__ == '__main__':
net.print_weights()
time.sleep(0.1)
print(net.is_fixpoint(epsilon=0.1e-6))
print()
net.learn(1, reduction=LearningNeuralNetwork.fft_reduction)
import time
time.sleep(0.1)
net.print_weights()
time.sleep(0.1)
print(net.is_fixpoint(epsilon=0.1e-6))
if True:
if False:
# ok so this works quite realiably
with FixpointExperiment() as exp:
for i in range(10):
for i in range(1):
run_count = 1000
net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\

8
code/soup.py
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@ -59,7 +59,7 @@ class Soup:
particle.attack(other_particle)
description['action'] = 'attacking'
description['counterpart'] = other_particle.get_uid()
if prng() < self.params.get('train_other_rate'):
if prng() < self.params.get('train_other_rate') and hasattr(self, 'train_other'):
other_particle_id = int(prng() * len(self.particles))
other_particle = self.particles[other_particle_id]
particle.train_other(other_particle)
@ -150,7 +150,7 @@ class ParticleDecorator:
if __name__ == '__main__':
if False:
if True:
with SoupExperiment() as exp:
for run_id in range(1):
net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
@ -159,11 +159,11 @@ if __name__ == '__main__':
# 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)):
for _ in tqdm(range(1000)):
soup.evolve()
exp.log(soup.count())
if True:
if False:
with SoupExperiment("soup") as exp:
for run_id in range(1):
net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)).with_keras_params(

17
code/visualization.py
View File

@ -14,7 +14,7 @@ import colorlover as cl
import dill
from sklearn.manifold.t_sne import TSNE
from sklearn.manifold.t_sne import TSNE, PCA
def build_args():
@ -31,8 +31,8 @@ def build_from_soup_or_exp(soup):
particle_dict = dict(
trajectory=[event['weights'] for event in particle],
time=[event['time'] for event in particle],
action=[event['action'] for event in particle],
counterpart=[event['counterpart'] for event in particle]
action=[event.get('action', None) for event in particle],
counterpart=[event.get('counterpart', None) for event in particle]
)
particle_list.append(particle_dict)
return particle_list
@ -101,15 +101,18 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
scale = cl.interp(bupu, len(data_list)+1) # Map color scale to N bins
# Fit the embedding space
transformer = TSNE()
transformer = PCA(n_components=2)
array = []
for particle_dict in data_list:
array = np.asarray(particle_dict['trajectory'])
transformer.fit(array)
array.append(particle_dict['trajectory'])
transformer.fit(np.vstack(array))
# Transform data accordingly and plot it
data = []
for p_id, particle_dict in enumerate(data_list):
transformed = transformer._fit(particle_dict['trajectory'])
transformed = transformer.transform(particle_dict['trajectory'])
line_trace = go.Scatter3d(
x=transformed[:, 0],
y=transformed[:, 1],