- rearanged experiments.py into single runable files

- Reformated net.self_x functions (sa, st)
- corrected robustness_exp.py
- NO DEBUGGING DONE!!!!!
This commit is contained in:
steffen-illium
2021-05-14 17:57:44 +02:00
parent 22d34d4e75
commit 4b5c36f6c0
10 changed files with 843 additions and 792 deletions

0
experiments/__init__.py Normal file

59
experiments/helpers.py Normal file

@ -0,0 +1,59 @@
""" ----------------------------------------- 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}/")

@ -0,0 +1,183 @@
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!!!')

@ -0,0 +1,154 @@
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!!!')

@ -0,0 +1,120 @@
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!!!')

@ -0,0 +1,114 @@
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

@ -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)