steffen/self-replicating-neural-networks
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Path Corrections for Multi os support

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Si11ium
2019-03-04 08:39:44 +01:00
parent 5295b51c28
commit f1a8d8184b
3 changed files with 41 additions and 38 deletions
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39
code/experiment.py
View File

@ -1,9 +1,9 @@
import sys
import os
import time
import copy
import dill
class Experiment:
@staticmethod
@ -11,22 +11,24 @@ class Experiment:
with open(path, "rb") as dill_file:
return dill.load(dill_file)
def __init__(self, name=None, id=None):
self.experiment_id = id or time.time()
this_file = os.path.realpath(os.getcwd() + "/" + sys.argv[0])
self.experiment_name = name or os.path.basename(this_file)
self.base_dir = os.path.realpath((os.path.dirname(this_file) + "/..")) + "/"
def __init__(self, name=None, ident=None):
self.experiment_id = ident or time.time()
# TODO reapair this path
this_file = os.path.realpath(os.getcwd())
# Was wolltest du hier tun? Vorher die nummer war unsinnig.
self.experiment_name = name or os.path.basename(this_file).split('.')[0]
self.base_dir = os.path.join(os.getcwd(), self.experiment_name)
self.next_iteration = 0
self.log_messages = []
self.initialize_more()
def initialize_more(self):
pass
def __enter__(self):
self.dir = self.base_dir + "experiments/exp-" + str(self.experiment_name) + "-" + str(self.experiment_id) + "-" + str(self.next_iteration) + "/"
os.mkdir(self.dir)
print("** created " + str(self.dir))
self.dir = os.path.join(self.base_dir, 'experiments', 'exp-{name}-{id}-{it}'.format(
name=self.experiment_name, id=self.experiment_id, it=self.next_iteration)
)
# Use makedirs for subfolder creation
os.makedirs(self.dir)
# os.mkdir(self.dir)
print("** created {dir} **".format(dir=self.dir))
return self
def __exit__(self, exc_type, exc_value, traceback):
@ -39,27 +41,30 @@ class Experiment:
print(message, **kwargs)
def save_log(self, log_name="log"):
with open(self.dir + "/" + str(log_name) + ".txt", "w") as log_file:
with open(os.path.join(self.dir, "{name}.txt".format(name=log_name)), "w") as log_file:
for log_message in self.log_messages:
print(str(log_message), file=log_file)
def save(self, **kwargs):
for name,value in kwargs.items():
with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
for name, value in kwargs.items():
with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
dill.dump(value, dill_file)
class FixpointExperiment(Experiment):
def initialize_more(self):
def __init__(self):
super().__init__()
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):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
i += 1
self.count(net)
def count(self, net):
if net.is_diverged():
self.counters['divergent'] += 1

2
code/network.py
View File

@ -314,7 +314,6 @@ class RecurrentNeuralNetwork(NeuralNetwork):
return new_weights
if __name__ == '__main__':
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
@ -324,4 +323,3 @@ if __name__ == '__main__':
# net.print_weights()
exp.run_net(net, 100)
exp.log(exp.counters)

38
code/test.py
View File

@ -10,23 +10,23 @@ def vary(e=0.0, f=0.0):
np.array([[1.0+e], [0.0+f]], dtype=np.float32)
]
net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
if False:
net.set_weights([
np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
np.array([[1.0], [0.0]], dtype=np.float32)
])
print(net.get_weights())
net.self_attack(100)
print(net.get_weights())
print(net.is_fixpoint())
if True:
net.set_weights(vary(0.01, 0.0))
print(net.get_weights())
for _ in range(5):
net.self_attack()
if __name__ == '__main__':
net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
if False:
net.set_weights([
np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
np.array([[1.0], [0.0]], dtype=np.float32)
])
print(net.get_weights())
print(net.is_fixpoint())
net.self_attack(100)
print(net.get_weights())
print(net.is_fixpoint())
if True:
net.set_weights(vary(0.01, 0.0))
print(net.get_weights())
for _ in range(5):
net.self_attack()
print(net.get_weights())
print(net.is_fixpoint())