steffen/self-replicating-neural-networks
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

All Experiments debugged

Browse Source 
ToDo:
 - convert strings in pathlib.Path objects
 - check usage of fixpoint tests
This commit is contained in:
steffen-illium 2021-05-16 15:34:43 +02:00
parent 36377ee27d
commit c1f58f2675
5 changed files with 21 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
experiments/mixed_setting_exp.py
View File

@ -68,17 +68,13 @@ class MixedSettingExperiment:
if self.train_nets == "before_SA":
for _ in range(self.ST_steps_between_SA):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
net.self_train(1, self.log_step_size, self.net_learning_rate)
net.self_application(self.SA_steps, self.log_step_size)
elif self.train_nets == "after_SA":
net.self_application(self.SA_steps, self.log_step_size)
for _ in range(self.ST_steps_between_SA):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
net.self_train(1, self.log_step_size, self.net_learning_rate)
print(
f"\nLast weight matrix (epoch: {j}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")

4
experiments/robustness_exp.py
View File

@ -63,8 +63,6 @@ class RobustnessExperiment:
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.ST_steps):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
net.self_train(1, self.log_step_size, self.net_learning_rate)
self.nets.append(net)
@ -86,7 +84,7 @@ class RobustnessExperiment:
# Extra safety for the value of the weights
original_net_clone.load_state_dict(copy.deepcopy(original_net.state_dict()))
noisy_weights = add_noise(original_net_clone.input_weight_matrix())
noisy_weights = add_noise(original_net_clone.input_weight_matrix(), epsilon=pow(10, -j))
original_net_clone.apply_weights(noisy_weights)
# Testing if the new net is still an identity function after applying noise

24
experiments/soup_exp.py
View File

@ -1,6 +1,8 @@
import random
import os.path
import pickle
from pathlib import Path
from typing import Union
from tqdm import tqdm
@ -12,7 +14,7 @@ from visualization import plot_loss, bar_chart_fixpoints, plot_3d_soup, line_cha
class SoupExperiment:
def __init__(self, population_size, net_i_size, net_h_size, net_o_size, learning_rate, attack_chance,
train_nets, ST_steps, epochs, log_step_size, directory_name):
train_nets, ST_steps, epochs, log_step_size, directory: Union[str, Path]):
super().__init__()
self.population_size = population_size
@ -40,8 +42,8 @@ class SoupExperiment:
# <self.fixpoint_counters_history> is used for keeping track of the amount of fixpoints in %
self.fixpoint_counters_history = []
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.directory = Path(directory)
self.directory.mkdir(parents=True, exist_ok=True)
self.population = []
self.populate_environment()
@ -69,8 +71,7 @@ class SoupExperiment:
loop_epochs.set_description("Evolving soup %s" % i)
# A network attacking another network with a given percentage
chance = random.randint(1, 100)
if chance <= self.attack_chance:
if random.randint(1, 100) <= self.attack_chance:
random_net1, random_net2 = random.sample(range(self.population_size), 2)
random_net1 = self.population[random_net1]
random_net2 = self.population[random_net2]
@ -91,24 +92,25 @@ class SoupExperiment:
self.fixpoint_counters["fix_sec"]) / self.population_size, 1)
self.fixpoint_counters_history.append(fixpoints_percentage)
# Resetting the fixpoint counter. Last iteration not to be reset - it is important for the bar_chart_fixpoints().
# Resetting the fixpoint counter. Last iteration not to be reset -
# it is important for the bar_chart_fixpoints().
if i < self.epochs:
self.reset_fixpoint_counters()
def weights_evolution_3d_experiment(self):
exp_name = f"soup_{self.population_size}_nets_{self.ST_steps}_training_{self.epochs}_epochs"
return plot_3d_soup(self.population, exp_name, self.directory_name)
return plot_3d_soup(self.population, exp_name, self.directory)
def count_fixpoints(self):
test_for_fixpoints(self.fixpoint_counters, self.population)
exp_details = f"Evolution steps: {self.epochs} epochs"
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory, self.net_learning_rate,
exp_details)
def fixpoint_percentage(self):
runs = self.epochs / self.ST_steps
SA_steps = None
line_chart_fixpoints(self.fixpoint_counters_history, runs, self.ST_steps, SA_steps, self.directory_name,
line_chart_fixpoints(self.fixpoint_counters_history, runs, self.ST_steps, SA_steps, self.directory,
self.population_size)
def visualize_loss(self):
@ -116,7 +118,7 @@ class SoupExperiment:
net_loss_history = self.population[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
plot_loss(self.loss_history, self.directory)
def reset_fixpoint_counters(self):
self.fixpoint_counters = {
@ -138,6 +140,7 @@ def run_soup_experiment(population_size, attack_chance, net_input_size, net_hidd
# Running the experiments
for i in range(runs):
# FIXME: Make this a pathlib.Path() Operation
directory_name = f"experiments/soup/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
soup_experiment = SoupExperiment(
@ -166,6 +169,7 @@ def run_soup_experiment(population_size, attack_chance, net_input_size, net_hidd
range(len(fixpoints_percentages))]
# Creating a folder for the summary of the current runs
# FIXME: Make this a pathlib.Path() Operation
directory_name = f"experiments/soup/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
os.mkdir(directory_name)

2
functionalities_test.py
View File

@ -54,7 +54,7 @@ def is_secondary_fixpoint(network: Net, epsilon: float = pow(10, -5)) -> bool:
def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
id_functions = id_functions or None
id_functions = id_functions or list()
for i in range(len(nets)):
net = nets[i]

4
main.py
View File

@ -40,11 +40,11 @@ if __name__ == '__main__':
# Constants:
NET_INPUT_SIZE = 4
NET_OUT_SIZE = 1
run_ST_experiment_bool = True
run_ST_experiment_bool = False
run_SA_experiment_bool = False
run_soup_experiment_bool = False
run_mixed_experiment_bool = False
run_robustness_bool = False
run_robustness_bool = True
""" ------------------------------------- Self-training (ST) experiment ------------------------------------- """