steffen/self-replicating-neural-networks
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Added distinction time-as-fixpoint and time-to-vergence to be tracked.

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Maximilian Zorn
2021-05-22 14:43:20 +02:00
parent e9f6620b60
commit b1dc574f5b
2 changed files with 19 additions and 11 deletions
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2
README.md
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@@ -13,6 +13,8 @@
- see `journal_robustness.py` for robustness test modeled after cristians robustness-exp (with the exeption that we put noise on the weights). Has `synthetic` bool to switch to hand-modeled perfect fixpoint instead of naturally trained ones.
- Also added two difference between the "time-as-fixpoint" and "time-to-verge" (i.e. to divergence / zero).
- We might need to consult about the "average loss per application step", as I think application loss get gradually higher the worse the weights get. So the average might not tell us much here.
- [ ] Adjust Self Training so that it favors second order fixpoints-> Second order test implementation (?)

28
journal_robustness.py
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@@ -70,7 +70,7 @@ class RobustnessComparisonExperiment:
self.id_functions = []
self.populate_environment()
self.count_fixpoints()
self.data = self.test_robustness()
self.time_to_vergence, self.time_as_fixpoint = self.test_robustness()
self.save()
@@ -82,13 +82,13 @@ class RobustnessComparisonExperiment:
if self.synthetic:
''' Either use perfect / hand-constructed fixpoint ... '''
net_name = f"ST_net_{str(i)}_synthetic"
net_name = f"net_{str(i)}_synthetic"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
net.apply_weights(generate_fixpoint_weights())
else:
''' .. or use natural approach to train fixpoints from random initialisation. '''
net_name = f"ST_net_{str(i)}"
net_name = f"net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.epochs):
for _ in range(self.ST_steps):
@@ -98,7 +98,8 @@ class RobustnessComparisonExperiment:
def test_robustness(self, print_it=True):
data = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
time_to_vergence = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
time_as_fixpoint = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
avg_loss_per_application = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
noise_range = range(10)
row_headers = []
@@ -115,19 +116,24 @@ class RobustnessComparisonExperiment:
clone = self.apply_noise(clone, rand_noise)
while not is_zero_fixpoint(clone) and not is_divergent(clone):
if is_identity_function(clone):
time_as_fixpoint[i][noise_level] += 1
# Todo: what kind of comparison between application? -> before
clone.self_application(1, self.log_step_size)
data[i][noise_level] += 1
time_to_vergence[i][noise_level] += 1
# -> after
if print_it:
print(f"Number appplications steps: ")
col_headers = [str(f"10e-{d}") for d in noise_range]
print(tabulate(data, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
# other tables here
return data
print(f"\nAppplications steps until divergence / zero: ")
print(tabulate(time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
print(f"\nTime as fixpoint: ")
print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
return time_as_fixpoint, time_to_vergence
def count_fixpoints(self):
@@ -156,7 +162,7 @@ if __name__ == "__main__":
ST_steps = 1000
ST_epochs = 5
ST_log_step_size = 10
ST_population_size = 3
ST_population_size = 5
ST_net_hidden_size = 2
ST_net_learning_rate = 0.04
ST_name_hash = random.getrandbits(32)