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- rearanged experiments.py into single runable files

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- Reformated net.self_x functions (sa, st)
- corrected robustness_exp.py
- NO DEBUGGING DONE!!!!!
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steffen-illium
2021-05-14 17:57:44 +02:00
parent 22d34d4e75
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import copy
import random
import os.path
import pickle
from tokenize import String
from tqdm import tqdm
from functionalities_test import test_for_fixpoints, is_zero_fixpoint, is_identity_function
from network import Net
from visualization import plot_loss, bar_chart_fixpoints, plot_3d_soup, line_chart_fixpoints, box_plot, write_file
from visualization import plot_3d_self_application, plot_3d_self_train
class SelfTrainExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, directory_name) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.epochs = epochs
self.loss_history = []
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
# Create population:
self.populate_environment()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating ST experiment %s" % i)
net_name = f"ST_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.epochs):
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)
print(f"\nLast weight matrix (epoch: {self.epochs}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
self.nets.append(net)
def weights_evolution_3d_experiment(self):
exp_name = f"ST_{str(len(self.nets))}_nets_3d_weights_PCA"
return plot_3d_self_train(self.nets, exp_name, self.directory_name, self.log_step_size)
def count_fixpoints(self):
test_for_fixpoints(self.fixpoint_counters, self.nets)
exp_details = f"Self-train for {self.epochs} epochs"
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
class SelfApplicationExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, application_steps, train_nets, directory_name, training_steps
) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.SA_steps = application_steps #
self.train_nets = train_nets
self.ST_steps = training_steps
self.directory_name = directory_name
os.mkdir(self.directory_name)
""" Creating the nets & making the SA steps & (maybe) also training the networks. """
self.nets = []
# Create population:
self.populate_environment()
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.weights_evolution_3d_experiment()
self.count_fixpoints()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating SA experiment %s" % i)
net_name = f"SA_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name
)
for _ in range(self.SA_steps):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
if self.train_nets == "before_SA":
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
net.self_application(input_data, self.SA_steps, self.log_step_size)
elif self.train_nets == "after_SA":
net.self_application(input_data, self.SA_steps, self.log_step_size)
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
else:
net.self_application(input_data, self.SA_steps, self.log_step_size)
self.nets.append(net)
def weights_evolution_3d_experiment(self):
exp_name = f"SA_{str(len(self.nets))}_nets_3d_weights_PCA"
plot_3d_self_application(self.nets, exp_name, self.directory_name, self.log_step_size)
def count_fixpoints(self):
test_for_fixpoints(self.fixpoint_counters, self.nets)
exp_details = f"{self.SA_steps} SA steps"
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
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):
super().__init__()
self.population_size = population_size
self.net_input_size = net_i_size
self.net_hidden_size = net_h_size
self.net_out_size = net_o_size
self.net_learning_rate = learning_rate
self.attack_chance = attack_chance
self.train_nets = train_nets
# self.SA_steps = SA_steps
self.ST_steps = ST_steps
self.epochs = epochs
self.log_step_size = log_step_size
self.loss_history = []
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
# <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.population = []
self.populate_environment()
self.evolve()
self.fixpoint_percentage()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in tqdm(range(self.population_size)):
loop_population_size.set_description("Populating soup experiment %s" % i)
net_name = f"soup_network_{i}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
self.population.append(net)
def evolve(self):
""" Evolving consists of attacking & self-training. """
loop_epochs = tqdm(range(self.epochs))
for i in loop_epochs:
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:
random_net1, random_net2 = random.sample(range(self.population_size), 2)
random_net1 = self.population[random_net1]
random_net2 = self.population[random_net2]
print(f"\n Attack: {random_net1.name} -> {random_net2.name}")
random_net1.attack(random_net2)
# Self-training each network in the population
for j in range(self.population_size):
net = self.population[j]
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, input_data, target_data)
# Testing for fixpoints after each batch of ST steps to see relevant data
if i % self.ST_steps == 0:
test_for_fixpoints(self.fixpoint_counters, self.population)
fixpoints_percentage = round((self.fixpoint_counters["fix_zero"] + self.fixpoint_counters["fix_weak"] +
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().
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)
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,
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,
self.population_size)
def visualize_loss(self):
for i in range(len(self.population)):
net_loss_history = self.population[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
def reset_fixpoint_counters(self):
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
class MixedSettingExperiment:
def __init__(self, population_size, net_i_size, net_h_size, net_o_size, learning_rate, train_nets,
epochs, SA_steps, ST_steps_between_SA, log_step_size, directory_name):
super().__init__()
self.population_size = population_size
self.net_input_size = net_i_size
self.net_hidden_size = net_h_size
self.net_out_size = net_o_size
self.net_learning_rate = learning_rate
self.train_nets = train_nets
self.epochs = epochs
self.SA_steps = SA_steps
self.ST_steps_between_SA = ST_steps_between_SA
self.log_step_size = log_step_size
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.loss_history = []
self.fixpoint_counters_history = []
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
self.populate_environment()
self.fixpoint_percentage()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating mixed experiment %s" % i)
net_name = f"mixed_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
self.nets.append(net)
loop_epochs = tqdm(range(self.epochs))
for j in loop_epochs:
loop_epochs.set_description("Running mixed experiment %s" % j)
for i in loop_population_size:
net = self.nets[i]
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)
input_data = net.input_weight_matrix()
net.self_application(input_data, self.SA_steps, self.log_step_size)
elif self.train_nets == "after_SA":
input_data = net.input_weight_matrix()
net.self_application(input_data, 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)
print(f"\nLast weight matrix (epoch: {j}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
test_for_fixpoints(self.fixpoint_counters, self.nets)
# Rounding the result not to run into other problems later regarding the exact representation of floating number
fixpoints_percentage = round((self.fixpoint_counters["fix_zero"] + 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().
if j < self.epochs:
self.reset_fixpoint_counters()
def weights_evolution_3d_experiment(self):
exp_name = f"Mixed {str(len(self.nets))}"
# This batch size is not relevant for mixed settings because during an epoch there are more steps of SA & ST happening
# and only they need the batch size. To not affect the number of epochs shown in the 3D plot, will send
# forward the number "1" for batch size with the variable <irrelevant_batch_size>
irrelevant_batch_size = 1
plot_3d_self_train(self.nets, exp_name, self.directory_name, irrelevant_batch_size)
def count_fixpoints(self):
exp_details = f"SA steps: {self.SA_steps}; ST steps: {self.ST_steps_between_SA}"
test_for_fixpoints(self.fixpoint_counters, self.nets)
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def fixpoint_percentage(self):
line_chart_fixpoints(self.fixpoint_counters_history, self.epochs, self.ST_steps_between_SA,
self.SA_steps, self.directory_name, self.population_size)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
def reset_fixpoint_counters(self):
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
class RobustnessExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
ST_steps, directory_name) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.ST_steps = ST_steps
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.id_functions = []
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
# Create population:
self.populate_environment()
print("Nets:\n", self.nets)
self.count_fixpoints()
[print(net.is_fixpoint) for net in self.nets]
self.test_robustness()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating robustness experiment %s" % 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.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, input_data, target_data)
self.nets.append(net)
def test_robustness(self):
#test_for_fixpoints(self.fixpoint_counters, self.nets, self.id_functions)
zero_epsilon = pow(10, -5)
data = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
for i in range(len(self.id_functions)):
for j in range(10):
original_net = self.id_functions[i]
# Creating a clone of the network. Not by copying it, but by creating a completely new network
# and changing its weights to the original ones.
original_net_clone = Net(original_net.input_size, original_net.hidden_size, original_net.out_size,
original_net.name)
# Extra safety for the value of the weights
original_net_clone.load_state_dict(copy.deepcopy(original_net.state_dict()))
input_data = original_net_clone.input_weight_matrix()
target_data = original_net_clone.create_target_weights(input_data)
changed_weights = copy.deepcopy(input_data)
for k in range(len(input_data)):
changed_weights[k][0] = changed_weights[k][0] + pow(10, -j)
# Testing if the new net is still an identity function after applying noise
still_id_func = is_identity_function(original_net_clone, changed_weights, target_data, zero_epsilon)
# If the net is still an id. func. after applying the first run of noise, continue to apply it until otherwise
while still_id_func and data[i][j] <= 1000:
data[i][j] += 1
input_data = original_net_clone.input_weight_matrix()
original_net_clone = original_net_clone.self_application(input_data, 1, self.log_step_size)
#new_weights = original_net_clone.create_target_weights(changed_weights)
#original_net_clone = original_net_clone.apply_weights(original_net_clone, new_weights)
still_id_func = is_identity_function(original_net_clone, input_data, target_data, zero_epsilon)
print(f"Data {data}")
if data.count(0) == 10:
print(f"There is no network resisting the robustness test.")
text = f"For this population of \n {self.population_size} networks \n there is no" \
f" network resisting the robustness test."
write_file(text, self.directory_name)
else:
box_plot(data, self.directory_name, self.population_size)
def count_fixpoints(self):
exp_details = f"ST steps: {self.ST_steps}"
self.id_functions = test_for_fixpoints(self.fixpoint_counters, self.nets)
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
""" ----------------------------------------------- Running the experiments ----------------------------------------------- """
def run_ST_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, runs, run_name, name_hash):
experiments = {}
check_folder("self_training")
# Running the experiments
for i in range(runs):
ST_directory_name = f"experiments/self_training/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
ST_experiment = SelfTrainExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
epochs,
ST_directory_name
)
pickle.dump(ST_experiment, open(f"{ST_directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = ST_experiment
# Building a summary of all the runs
directory_name = f"experiments/self_training/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "ST"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
def run_SA_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, runs, run_name, name_hash, application_steps, train_nets, training_steps):
experiments = {}
check_folder("self_application")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/self_application/{run_name}_run_{i}_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
SA_experiment = SelfApplicationExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
application_steps,
train_nets,
directory_name,
training_steps
)
pickle.dump(SA_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = SA_experiment
# Building a summary of all the runs
directory_name = f"experiments/self_application/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "SA"
summary_fixpoint_experiment(runs, population_size, application_steps, experiments, net_learning_rate,
directory_name,
summary_pre_title)
def run_soup_experiment(population_size, attack_chance, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, epochs, batch_size, runs, run_name, name_hash, ST_steps, train_nets):
experiments = {}
fixpoints_percentages = []
check_folder("soup")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/soup/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
soup_experiment = SoupExperiment(
population_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
attack_chance,
train_nets,
ST_steps,
epochs,
batch_size,
directory_name
)
pickle.dump(soup_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = soup_experiment
# Building history of fixpoint percentages for summary
fixpoint_counters_history = soup_experiment.fixpoint_counters_history
if not fixpoints_percentages:
fixpoints_percentages = soup_experiment.fixpoint_counters_history
else:
# Using list comprehension to make the sum of all the percentages
fixpoints_percentages = [fixpoints_percentages[i] + fixpoint_counters_history[i] for i in
range(len(fixpoints_percentages))]
# Creating a folder for the summary of the current runs
directory_name = f"experiments/soup/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
os.mkdir(directory_name)
# Building a summary of all the runs
summary_pre_title = "soup"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
SA_steps = None
summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps, SA_steps, directory_name,
population_size)
def run_mixed_experiment(population_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate, train_nets,
epochs, SA_steps, ST_steps_between_SA, batch_size, name_hash, runs, run_name):
experiments = {}
fixpoints_percentages = []
check_folder("mixed")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/mixed/{run_name}_run_{i}_{str(population_size)}_nets_{SA_steps}_SA_{ST_steps_between_SA}_ST_{str(name_hash)}"
mixed_experiment = MixedSettingExperiment(
population_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
train_nets,
epochs,
SA_steps,
ST_steps_between_SA,
batch_size,
directory_name
)
pickle.dump(mixed_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = mixed_experiment
# Building history of fixpoint percentages for summary
fixpoint_counters_history = mixed_experiment.fixpoint_counters_history
if not fixpoints_percentages:
fixpoints_percentages = mixed_experiment.fixpoint_counters_history
else:
# Using list comprehension to make the sum of all the percentages
fixpoints_percentages = [fixpoints_percentages[i] + fixpoint_counters_history[i] for i in
range(len(fixpoints_percentages))]
# Building a summary of all the runs
directory_name = f"experiments/mixed/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "mixed"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps_between_SA, SA_steps, directory_name,
population_size)
def run_robustness_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, epochs, runs, run_name, name_hash):
experiments = {}
check_folder("robustness")
# Running the experiments
for i in range(runs):
ST_directory_name = f"experiments/robustness/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
robustness_experiment = RobustnessExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
epochs,
ST_directory_name
)
pickle.dump(robustness_experiment, open(f"{ST_directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = robustness_experiment
# Building a summary of all the runs
directory_name = f"experiments/robustness/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "robustness"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
""" ----------------------------------------- Methods for summarizing the experiments ------------------------------------------ """
def summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title):
avg_fixpoint_counters = {
"avg_identity_func": 0,
"avg_divergent": 0,
"avg_fix_zero": 0,
"avg_fix_weak": 0,
"avg_fix_sec": 0,
"avg_other_func": 0
}
for i in range(len(experiments)):
fixpoint_counters = experiments[i].fixpoint_counters
avg_fixpoint_counters["avg_identity_func"] += fixpoint_counters["identity_func"]
avg_fixpoint_counters["avg_divergent"] += fixpoint_counters["divergent"]
avg_fixpoint_counters["avg_fix_zero"] += fixpoint_counters["fix_zero"]
avg_fixpoint_counters["avg_fix_weak"] += fixpoint_counters["fix_weak"]
avg_fixpoint_counters["avg_fix_sec"] += fixpoint_counters["fix_sec"]
avg_fixpoint_counters["avg_other_func"] += fixpoint_counters["other_func"]
# Calculating the average for each fixpoint
avg_fixpoint_counters.update((x, y / len(experiments)) for x, y in avg_fixpoint_counters.items())
# Checking where the data is coming from to have a relevant title in the plot.
if summary_pre_title not in ["ST", "SA", "soup", "mixed", "robustness"]:
summary_pre_title = ""
# Plotting the summary
source_checker = "summary"
exp_details = f"{summary_pre_title}: {runs} runs & {epochs} epochs each."
bar_chart_fixpoints(avg_fixpoint_counters, population_size, directory_name, net_learning_rate, exp_details,
source_checker)
def summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps, SA_steps, directory_name,
population_size):
fixpoints_percentages = [round(fixpoints_percentages[i] / runs, 1) for i in range(len(fixpoints_percentages))]
# Plotting summary
if "soup" in directory_name:
line_chart_fixpoints(fixpoints_percentages, epochs / ST_steps, ST_steps, SA_steps, directory_name,
population_size)
else:
line_chart_fixpoints(fixpoints_percentages, epochs, ST_steps, SA_steps, directory_name, population_size)
""" --------------------------------------------------- Miscellaneous ---------------------------------------------------------- """
def check_folder(experiment_folder: String):
if not os.path.isdir("experiments"): os.mkdir(f"experiments/")
if not os.path.isdir(f"experiments/{experiment_folder}/"): os.mkdir(f"experiments/{experiment_folder}/")

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""" ----------------------------------------- Methods for summarizing the experiments ------------------------------------------ """
import os
from visualization import line_chart_fixpoints, bar_chart_fixpoints
def summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title):
avg_fixpoint_counters = {
"avg_identity_func": 0,
"avg_divergent": 0,
"avg_fix_zero": 0,
"avg_fix_weak": 0,
"avg_fix_sec": 0,
"avg_other_func": 0
}
for i in range(len(experiments)):
fixpoint_counters = experiments[i].fixpoint_counters
avg_fixpoint_counters["avg_identity_func"] += fixpoint_counters["identity_func"]
avg_fixpoint_counters["avg_divergent"] += fixpoint_counters["divergent"]
avg_fixpoint_counters["avg_fix_zero"] += fixpoint_counters["fix_zero"]
avg_fixpoint_counters["avg_fix_weak"] += fixpoint_counters["fix_weak"]
avg_fixpoint_counters["avg_fix_sec"] += fixpoint_counters["fix_sec"]
avg_fixpoint_counters["avg_other_func"] += fixpoint_counters["other_func"]
# Calculating the average for each fixpoint
avg_fixpoint_counters.update((x, y / len(experiments)) for x, y in avg_fixpoint_counters.items())
# Checking where the data is coming from to have a relevant title in the plot.
if summary_pre_title not in ["ST", "SA", "soup", "mixed", "robustness"]:
summary_pre_title = ""
# Plotting the summary
source_checker = "summary"
exp_details = f"{summary_pre_title}: {runs} runs & {epochs} epochs each."
bar_chart_fixpoints(avg_fixpoint_counters, population_size, directory_name, net_learning_rate, exp_details,
source_checker)
def summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps, SA_steps, directory_name,
population_size):
fixpoints_percentages = [round(fixpoints_percentages[i] / runs, 1) for i in range(len(fixpoints_percentages))]
# Plotting summary
if "soup" in directory_name:
line_chart_fixpoints(fixpoints_percentages, epochs / ST_steps, ST_steps, SA_steps, directory_name,
population_size)
else:
line_chart_fixpoints(fixpoints_percentages, epochs, ST_steps, SA_steps, directory_name, population_size)
""" --------------------------------------------------- Miscellaneous ---------------------------------------------------------- """
def check_folder(experiment_folder: str):
if not os.path.isdir("experiments"): os.mkdir(f"experiments/")
if not os.path.isdir(f"experiments/{experiment_folder}/"): os.mkdir(f"experiments/{experiment_folder}/")

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import os.path
import pickle
from tqdm import tqdm
from experiments.helpers import check_folder, summary_fixpoint_experiment, summary_fixpoint_percentage
from functionalities_test import test_for_fixpoints
from network import Net
from visualization import plot_loss, bar_chart_fixpoints, line_chart_fixpoints
from visualization import plot_3d_self_train
class MixedSettingExperiment:
def __init__(self, population_size, net_i_size, net_h_size, net_o_size, learning_rate, train_nets,
epochs, SA_steps, ST_steps_between_SA, log_step_size, directory_name):
super().__init__()
self.population_size = population_size
self.net_input_size = net_i_size
self.net_hidden_size = net_h_size
self.net_out_size = net_o_size
self.net_learning_rate = learning_rate
self.train_nets = train_nets
self.epochs = epochs
self.SA_steps = SA_steps
self.ST_steps_between_SA = ST_steps_between_SA
self.log_step_size = log_step_size
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.loss_history = []
self.fixpoint_counters_history = []
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
self.populate_environment()
self.fixpoint_percentage()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating mixed experiment %s" % i)
net_name = f"mixed_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
self.nets.append(net)
loop_epochs = tqdm(range(self.epochs))
for j in loop_epochs:
loop_epochs.set_description("Running mixed experiment %s" % j)
for i in loop_population_size:
net = self.nets[i]
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)
input_data = net.input_weight_matrix()
net.self_application(input_data, self.SA_steps, self.log_step_size)
elif self.train_nets == "after_SA":
input_data = net.input_weight_matrix()
net.self_application(input_data, 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)
print(
f"\nLast weight matrix (epoch: {j}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
test_for_fixpoints(self.fixpoint_counters, self.nets)
# Rounding the result not to run into other problems later regarding the exact representation of floating number
fixpoints_percentage = round((self.fixpoint_counters["fix_zero"] + 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().
if j < self.epochs:
self.reset_fixpoint_counters()
def weights_evolution_3d_experiment(self):
exp_name = f"Mixed {str(len(self.nets))}"
# This batch size is not relevant for mixed settings because during an epoch there are more steps of SA & ST happening
# and only they need the batch size. To not affect the number of epochs shown in the 3D plot, will send
# forward the number "1" for batch size with the variable <irrelevant_batch_size>
irrelevant_batch_size = 1
plot_3d_self_train(self.nets, exp_name, self.directory_name, irrelevant_batch_size)
def count_fixpoints(self):
exp_details = f"SA steps: {self.SA_steps}; ST steps: {self.ST_steps_between_SA}"
test_for_fixpoints(self.fixpoint_counters, self.nets)
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def fixpoint_percentage(self):
line_chart_fixpoints(self.fixpoint_counters_history, self.epochs, self.ST_steps_between_SA,
self.SA_steps, self.directory_name, self.population_size)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
def reset_fixpoint_counters(self):
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
def run_mixed_experiment(population_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate, train_nets,
epochs, SA_steps, ST_steps_between_SA, batch_size, name_hash, runs, run_name):
experiments = {}
fixpoints_percentages = []
check_folder("mixed")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/mixed/{run_name}_run_{i}_{str(population_size)}_nets_{SA_steps}_SA_{ST_steps_between_SA}_ST_{str(name_hash)}"
mixed_experiment = MixedSettingExperiment(
population_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
train_nets,
epochs,
SA_steps,
ST_steps_between_SA,
batch_size,
directory_name
)
pickle.dump(mixed_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = mixed_experiment
# Building history of fixpoint percentages for summary
fixpoint_counters_history = mixed_experiment.fixpoint_counters_history
if not fixpoints_percentages:
fixpoints_percentages = mixed_experiment.fixpoint_counters_history
else:
# Using list comprehension to make the sum of all the percentages
fixpoints_percentages = [fixpoints_percentages[i] + fixpoint_counters_history[i] for i in
range(len(fixpoints_percentages))]
# Building a summary of all the runs
directory_name = f"experiments/mixed/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "mixed"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps_between_SA, SA_steps, directory_name,
population_size)
if __name__ == '__main__':
raise NotImplementedError('Test this here!!!')

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import copy
import os.path
import pickle
import random
from tqdm import tqdm
from experiments.helpers import check_folder, summary_fixpoint_experiment
from functionalities_test import test_for_fixpoints, is_identity_function
from network import Net
from visualization import bar_chart_fixpoints, box_plot, write_file
def add_noise(input_data, epsilon = pow(10, -5)):
output = copy.deepcopy(input_data)
for k in range(len(input_data)):
output[k][0] += random.random() * epsilon
return output
class RobustnessExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
ST_steps, directory_name) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.ST_steps = ST_steps
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.id_functions = []
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
# Create population:
self.populate_environment()
print("Nets:\n", self.nets)
self.count_fixpoints()
[print(net.is_fixpoint) for net in self.nets]
self.test_robustness()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating robustness experiment %s" % 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.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)
def test_robustness(self):
# test_for_fixpoints(self.fixpoint_counters, self.nets, self.id_functions)
zero_epsilon = pow(10, -5)
data = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
for i in range(len(self.id_functions)):
for j in range(10):
original_net = self.id_functions[i]
# Creating a clone of the network. Not by copying it, but by creating a completely new network
# and changing its weights to the original ones.
original_net_clone = Net(original_net.input_size, original_net.hidden_size, original_net.out_size,
original_net.name)
# 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())
original_net_clone.apply_weights(noisy_weights)
# Testing if the new net is still an identity function after applying noise
still_id_func = is_identity_function(original_net_clone, zero_epsilon)
# If the net is still an id. func. after applying the first run of noise, continue to apply it until otherwise
while still_id_func and data[i][j] <= 1000:
data[i][j] += 1
original_net_clone = original_net_clone.self_application(1, self.log_step_size)
still_id_func = is_identity_function(original_net_clone, zero_epsilon)
print(f"Data {data}")
if data.count(0) == 10:
print(f"There is no network resisting the robustness test.")
text = f"For this population of \n {self.population_size} networks \n there is no" \
f" network resisting the robustness test."
write_file(text, self.directory_name)
else:
box_plot(data, self.directory_name, self.population_size)
def count_fixpoints(self):
exp_details = f"ST steps: {self.ST_steps}"
self.id_functions = test_for_fixpoints(self.fixpoint_counters, self.nets)
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def run_robustness_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, epochs, runs, run_name, name_hash):
experiments = {}
check_folder("robustness")
# Running the experiments
for i in range(runs):
ST_directory_name = f"experiments/robustness/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
robustness_experiment = RobustnessExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
epochs,
ST_directory_name
)
pickle.dump(robustness_experiment, open(f"{ST_directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = robustness_experiment
# Building a summary of all the runs
directory_name = f"experiments/robustness/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "robustness"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
if __name__ == '__main__':
raise NotImplementedError('Test this here!!!')

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import os.path
import pickle
from tqdm import tqdm
from experiments.helpers import check_folder, summary_fixpoint_experiment
from functionalities_test import test_for_fixpoints
from network import Net
from visualization import bar_chart_fixpoints
from visualization import plot_3d_self_application
class SelfApplicationExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, application_steps, train_nets, directory_name, training_steps
) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.SA_steps = application_steps #
self.train_nets = train_nets
self.ST_steps = training_steps
self.directory_name = directory_name
os.mkdir(self.directory_name)
""" Creating the nets & making the SA steps & (maybe) also training the networks. """
self.nets = []
# Create population:
self.populate_environment()
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.weights_evolution_3d_experiment()
self.count_fixpoints()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating SA experiment %s" % i)
net_name = f"SA_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name
)
for _ in range(self.SA_steps):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
if self.train_nets == "before_SA":
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)
net.self_train(1, self.log_step_size, self.net_learning_rate)
else:
net.self_application(self.SA_steps, self.log_step_size)
self.nets.append(net)
def weights_evolution_3d_experiment(self):
exp_name = f"SA_{str(len(self.nets))}_nets_3d_weights_PCA"
plot_3d_self_application(self.nets, exp_name, self.directory_name, self.log_step_size)
def count_fixpoints(self):
test_for_fixpoints(self.fixpoint_counters, self.nets)
exp_details = f"{self.SA_steps} SA steps"
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def run_SA_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, runs, run_name, name_hash, application_steps, train_nets, training_steps):
experiments = {}
check_folder("self_application")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/self_application/{run_name}_run_{i}_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
SA_experiment = SelfApplicationExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
application_steps,
train_nets,
directory_name,
training_steps
)
pickle.dump(SA_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = SA_experiment
# Building a summary of all the runs
directory_name = f"experiments/self_application/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "SA"
summary_fixpoint_experiment(runs, population_size, application_steps, experiments, net_learning_rate,
directory_name,
summary_pre_title)
if __name__ == '__main__':
raise NotImplementedError('Test this here!!!')

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import os.path
import pickle
from tqdm import tqdm
from experiments.helpers import check_folder, summary_fixpoint_experiment
from functionalities_test import test_for_fixpoints
from network import Net
from visualization import plot_loss, bar_chart_fixpoints
from visualization import plot_3d_self_train
class SelfTrainExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, directory_name) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.epochs = epochs
self.loss_history = []
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.nets = []
# Create population:
self.populate_environment()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating ST experiment %s" % i)
net_name = f"ST_net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.epochs):
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)
print(f"\nLast weight matrix (epoch: {self.epochs}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
self.nets.append(net)
def weights_evolution_3d_experiment(self):
exp_name = f"ST_{str(len(self.nets))}_nets_3d_weights_PCA"
return plot_3d_self_train(self.nets, exp_name, self.directory_name, self.log_step_size)
def count_fixpoints(self):
test_for_fixpoints(self.fixpoint_counters, self.nets)
exp_details = f"Self-train for {self.epochs} epochs"
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
exp_details)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
def run_ST_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, runs, run_name, name_hash):
experiments = {}
check_folder("self_training")
# Running the experiments
for i in range(runs):
ST_directory_name = f"experiments/self_training/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
ST_experiment = SelfTrainExperiment(
population_size,
batch_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
epochs,
ST_directory_name
)
pickle.dump(ST_experiment, open(f"{ST_directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = ST_experiment
# Building a summary of all the runs
directory_name = f"experiments/self_training/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
os.mkdir(directory_name)
summary_pre_title = "ST"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
if __name__ == '__main__':
raise NotImplementedError('Test this here!!!')

179
experiments/soup_exp.py Normal file
View File

@ -0,0 +1,179 @@
import random
import os.path
import pickle
from tqdm import tqdm
from experiments.helpers import check_folder, summary_fixpoint_percentage, summary_fixpoint_experiment
from functionalities_test import test_for_fixpoints
from network import Net
from visualization import plot_loss, bar_chart_fixpoints, plot_3d_soup, line_chart_fixpoints
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):
super().__init__()
self.population_size = population_size
self.net_input_size = net_i_size
self.net_hidden_size = net_h_size
self.net_out_size = net_o_size
self.net_learning_rate = learning_rate
self.attack_chance = attack_chance
self.train_nets = train_nets
# self.SA_steps = SA_steps
self.ST_steps = ST_steps
self.epochs = epochs
self.log_step_size = log_step_size
self.loss_history = []
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
# <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.population = []
self.populate_environment()
self.evolve()
self.fixpoint_percentage()
self.weights_evolution_3d_experiment()
self.count_fixpoints()
self.visualize_loss()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
for i in tqdm(range(self.population_size)):
loop_population_size.set_description("Populating soup experiment %s" % i)
net_name = f"soup_network_{i}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
self.population.append(net)
def evolve(self):
""" Evolving consists of attacking & self-training. """
loop_epochs = tqdm(range(self.epochs))
for i in loop_epochs:
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:
random_net1, random_net2 = random.sample(range(self.population_size), 2)
random_net1 = self.population[random_net1]
random_net2 = self.population[random_net2]
print(f"\n Attack: {random_net1.name} -> {random_net2.name}")
random_net1.attack(random_net2)
# Self-training each network in the population
for j in range(self.population_size):
net = self.population[j]
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
# Testing for fixpoints after each batch of ST steps to see relevant data
if i % self.ST_steps == 0:
test_for_fixpoints(self.fixpoint_counters, self.population)
fixpoints_percentage = round((self.fixpoint_counters["fix_zero"] + self.fixpoint_counters["fix_weak"] +
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().
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)
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,
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,
self.population_size)
def visualize_loss(self):
for i in range(len(self.population)):
net_loss_history = self.population[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
def reset_fixpoint_counters(self):
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
def run_soup_experiment(population_size, attack_chance, net_input_size, net_hidden_size, net_out_size,
net_learning_rate, epochs, batch_size, runs, run_name, name_hash, ST_steps, train_nets):
experiments = {}
fixpoints_percentages = []
check_folder("soup")
# Running the experiments
for i in range(runs):
directory_name = f"experiments/soup/{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
soup_experiment = SoupExperiment(
population_size,
net_input_size,
net_hidden_size,
net_out_size,
net_learning_rate,
attack_chance,
train_nets,
ST_steps,
epochs,
batch_size,
directory_name
)
pickle.dump(soup_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
experiments[i] = soup_experiment
# Building history of fixpoint percentages for summary
fixpoint_counters_history = soup_experiment.fixpoint_counters_history
if not fixpoints_percentages:
fixpoints_percentages = soup_experiment.fixpoint_counters_history
else:
# Using list comprehension to make the sum of all the percentages
fixpoints_percentages = [fixpoints_percentages[i] + fixpoint_counters_history[i] for i in
range(len(fixpoints_percentages))]
# Creating a folder for the summary of the current runs
directory_name = f"experiments/soup/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
os.mkdir(directory_name)
# Building a summary of all the runs
summary_pre_title = "soup"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_pre_title)
SA_steps = None
summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps, SA_steps, directory_name,
population_size)

12
functionalities_test.py
View File

@ -27,7 +27,10 @@ def is_divergent(network: Net) -> bool:
return False return False
def is_identity_function(network: Net, input_data: Tensor, target_data: Tensor, epsilon=pow(10, -5)) -> bool: def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
input_data = network.input_weight_matrix()
target_data = network.create_target_weights(input_data)
predicted_values = network(input_data) predicted_values = network(input_data)
return np.allclose(target_data.detach().numpy(), predicted_values.detach().numpy(), 0, epsilon) return np.allclose(target_data.detach().numpy(), predicted_values.detach().numpy(), 0, epsilon)
@ -48,7 +51,7 @@ def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> b
# Getting the second output by initializing a new net with the weights of the original net. # Getting the second output by initializing a new net with the weights of the original net.
net_copy = copy.deepcopy(network) net_copy = copy.deepcopy(network)
net_copy.apply_weights(net_copy, first_output) net_copy.apply_weights(first_output)
input_data_2 = net_copy.input_weight_matrix() input_data_2 = net_copy.input_weight_matrix()
# Calculating second output # Calculating second output
@ -69,7 +72,8 @@ def is_weak_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
return result return result
def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=[]): def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
id_functions = id_functions or None
zero_epsilon = pow(10, -5) zero_epsilon = pow(10, -5)
epsilon = pow(10, -3) epsilon = pow(10, -3)
@ -81,7 +85,7 @@ def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=[]):
if is_divergent(nets[i]): if is_divergent(nets[i]):
fixpoint_counter["divergent"] += 1 fixpoint_counter["divergent"] += 1
nets[i].is_fixpoint = "divergent" nets[i].is_fixpoint = "divergent"
elif is_identity_function(nets[i], input_data, target_data, zero_epsilon): elif is_identity_function(nets[i], zero_epsilon):
fixpoint_counter["identity_func"] += 1 fixpoint_counter["identity_func"] += 1
nets[i].is_fixpoint = "identity_func" nets[i].is_fixpoint = "identity_func"
id_functions.append(nets[i]) id_functions.append(nets[i])

59
network.py
View File

@ -1,5 +1,7 @@
#from __future__ import annotations #from __future__ import annotations
import copy import copy
from typing import Union
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
@ -32,22 +34,16 @@ class Net(nn.Module):
return True return True
return False return False
@staticmethod def apply_weights(self, new_weights: Tensor):
def apply_weights(network, new_weights: Tensor):
""" Changing the weights of a network to new given values. """ """ Changing the weights of a network to new given values. """
i = 0 i = 0
for layer_id, layer_name in enumerate(self.state_dict()):
for layer_id, layer_name in enumerate(network.state_dict()): for line_id, line_values in enumerate(self.state_dict()[layer_name]):
for line_id, line_values in enumerate(network.state_dict()[layer_name]): for weight_id, weight_value in enumerate(self.state_dict()[layer_name][line_id]):
for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]): self.state_dict()[layer_name][line_id][weight_id] = new_weights[i]
network.state_dict()[layer_name][line_id][weight_id] = new_weights[i]
i += 1 i += 1
return network return self
def __init__(self, i_size: int, h_size: int, o_size: int, name=None, start_time=1) -> None: def __init__(self, i_size: int, h_size: int, o_size: int, name=None, start_time=1) -> None:
super().__init__() super().__init__()
@ -105,15 +101,17 @@ class Net(nn.Module):
return torch.from_numpy(weight_matrix) return torch.from_numpy(weight_matrix)
def self_train(self, training_steps: int, log_step_size: int, learning_rate: float, input_data: Tensor, target_data: Tensor) -> (np.ndarray, Tensor, list): def self_train(self, training_steps: int, log_step_size: int, learning_rate: float) -> (np.ndarray, Tensor, list):
""" Training a network to predict its own weights in order to self-replicate. """ """ Training a network to predict its own weights in order to self-replicate. """
optimizer = optim.SGD(self.parameters(), lr=learning_rate, momentum=0.9) optimizer = optim.SGD(self.parameters(), lr=learning_rate, momentum=0.9)
self.trained = True self.trained = True
for training_step in range(training_steps): for training_step in range(training_steps):
self.number_trained +=1 self.number_trained += 1
optimizer.zero_grad() optimizer.zero_grad()
input_data = self.input_weight_matrix()
target_data = self.create_target_weights(input_data)
output = self(input_data) output = self(input_data)
loss = F.mse_loss(output, target_data) loss = F.mse_loss(output, target_data)
loss.backward() loss.backward()
@ -122,31 +120,29 @@ class Net(nn.Module):
# Saving the history of the weights after a certain amount of steps (aka log_step_size) for research. # Saving the history of the weights after a certain amount of steps (aka log_step_size) for research.
# If it is a soup/mixed env. save weights only at the end of all training steps (aka a soup/mixed epoch) # If it is a soup/mixed env. save weights only at the end of all training steps (aka a soup/mixed epoch)
if "soup" not in self.name and "mixed" not in self.name: if "soup" not in self.name and "mixed" not in self.name:
weights = self.create_target_weights(self.input_weight_matrix())
# If self-training steps are lower than 10, then append weight history after each ST step. # If self-training steps are lower than 10, then append weight history after each ST step.
if self.number_trained < 10: if self.number_trained < 10:
self.s_train_weights_history.append(target_data.T.detach().numpy()) self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item()) self.loss_history.append(loss.detach().numpy().item())
else: else:
if self.number_trained % log_step_size == 0: if self.number_trained % log_step_size == 0:
self.s_train_weights_history.append(target_data.T.detach().numpy()) self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item()) self.loss_history.append(loss.detach().numpy().item())
weights = self.create_target_weights(self.input_weight_matrix())
# Saving weights only at the end of a soup/mixed exp. epoch. # Saving weights only at the end of a soup/mixed exp. epoch.
if "soup" in self.name or "mixed" in self.name: if "soup" in self.name or "mixed" in self.name:
self.s_train_weights_history.append(target_data.T.detach().numpy()) self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item()) self.loss_history.append(loss.detach().numpy().item())
return output.detach().numpy(), loss, self.loss_history return weights.detach().numpy(), loss, self.loss_history
def self_application(self, weights_matrix: Tensor, SA_steps: int, log_step_size: int) : def self_application(self, SA_steps: int, log_step_size: Union[int, None] = None):
""" Inputting the weights of a network to itself for a number of steps, without backpropagation. """ """ Inputting the weights of a network to itself for a number of steps, without backpropagation. """
data = copy.deepcopy(weights_matrix)
new_net = copy.deepcopy(self)
# output = new_net(data)
for i in range(SA_steps): for i in range(SA_steps):
output = new_net(data) output = self(self.input_weight_matrix())
# Saving the weights history after a certain amount of steps (aka log_step_size) for research purposes. # Saving the weights history after a certain amount of steps (aka log_step_size) for research purposes.
# If self-application steps are lower than 10, then append weight history after each SA step. # If self-application steps are lower than 10, then append weight history after each SA step.
@ -159,17 +155,14 @@ class Net(nn.Module):
""" See after how many steps of SA is the output not changing anymore: """ """ See after how many steps of SA is the output not changing anymore: """
# print(f"Self-app. step {i+1}: {Experiment.changing_rate(output2, output)}") # print(f"Self-app. step {i+1}: {Experiment.changing_rate(output2, output)}")
for j in range(len(data)): self = self.apply_weights(output)
""" Constructing the weight matrix to have it as the next input. """
data[j][0] = output[j]
new_net = self.apply_weights(new_net, output) return self
return new_net
def attack(self, other_net): def attack(self, other_net):
other_net_weights = other_net.input_weight_matrix() other_net_weights = other_net.input_weight_matrix()
SA_steps = 1 my_evaluation = self(other_net_weights)
log_step_size = 1
return self.self_application(other_net_weights, SA_steps, log_step_size) SA_steps = 1
return other_net.apply_weights(my_evaluation)