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

functionalities_test.py updated

Browse Source
This commit is contained in:
steffen-illium
2021-05-16 14:51:21 +02:00
parent b1472479cb
commit 36377ee27d
7 changed files with 66 additions and 97 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
experiments/__init__.py
View File

@@ -1,5 +1,5 @@
from mixed_setting_exp import run_mixed_experiment
from robustness_exp import run_robustness_experiment
from self_application_exp import run_SA_experiment
from self_train_exp import run_ST_experiment
from soup_exp import run_soup_experiment
from .mixed_setting_exp import run_mixed_experiment
from .robustness_exp import run_robustness_experiment
from .self_application_exp import run_SA_experiment
from .self_train_exp import run_ST_experiment
from .soup_exp import run_soup_experiment

4
experiments/helpers.py
View File

@@ -5,7 +5,7 @@ from pathlib import Path
from visualization import line_chart_fixpoints, bar_chart_fixpoints
def summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
def summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory,
summary_pre_title):
avg_fixpoint_counters = {
"avg_identity_func": 0,
@@ -36,7 +36,7 @@ def summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_
# 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,
bar_chart_fixpoints(avg_fixpoint_counters, population_size, directory, net_learning_rate, exp_details,
source_checker)

6
experiments/mixed_setting_exp.py
View File

@@ -71,12 +71,10 @@ class MixedSettingExperiment:
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)
net.self_application(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)
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)

21
experiments/self_train_exp.py
View File

@@ -1,5 +1,6 @@
import os.path
import pickle
from pathlib import Path
from tqdm import tqdm
@@ -82,13 +83,13 @@ class SelfTrainExperiment:
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")
logging_directory = Path('output') / 'self_training'
logging_directory.mkdir(parents=True, exist_ok=True)
# 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)}"
experiment_name = f"{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
this_exp_directory = logging_directory / experiment_name
ST_experiment = SelfTrainExperiment(
population_size,
batch_size,
@@ -97,17 +98,19 @@ def run_ST_experiment(population_size, batch_size, net_input_size, net_hidden_si
net_out_size,
net_learning_rate,
epochs,
ST_directory_name
this_exp_directory
)
pickle.dump(ST_experiment, open(f"{ST_directory_name}/full_experiment_pickle.p", "wb"))
with (this_exp_directory / 'full_experiment_pickle.p').open('wb') as f:
pickle.dump(ST_experiment, f)
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_name = f"/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
summary_directory_name = logging_directory / summary_name
summary_directory_name.mkdir(parents=True, exist_ok=True)
summary_pre_title = "ST"
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, summary_directory_name,
summary_pre_title)
if __name__ == '__main__':

62
functionalities_test.py
View File

@@ -5,25 +5,12 @@ from torch import Tensor
from network import Net
def overall_fixpoint_test(network: Net, epsilon: float, input_data) -> bool:
predicted_values = network(input_data)
check_smaller_epsilon = all(epsilon > predicted_values)
check_greater_epsilon = all(-epsilon < predicted_values)
if check_smaller_epsilon and check_greater_epsilon:
return True
else:
return False
def is_divergent(network: Net) -> bool:
for i in network.input_weight_matrix():
weight_value = i[0].item()
if np.isnan(weight_value) or np.isinf(weight_value):
if np.isnan(weight_value).all() or np.isinf(weight_value).all():
return True
return False
@@ -33,26 +20,26 @@ def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
target_data = network.create_target_weights(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(),
rtol=0, atol=epsilon)
def is_zero_fixpoint(network: Net, input_data: Tensor, epsilon=pow(10, -5)) -> bool:
# FIXME: Is the the correct test?
raise NotImplementedError
result = overall_fixpoint_test(network, epsilon, input_data)
def is_zero_fixpoint(network: Net) -> bool:
result = bool(len(np.nonzero(network.create_target_weights(network.input_weight_matrix()))))
return result
def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
def is_secondary_fixpoint(network: Net, epsilon: float = pow(10, -5)) -> bool:
""" Secondary fixpoint check is done like this: compare first INPUT with second OUTPUT.
If they are within the boundaries, then is secondary fixpoint. """
input_data = network.input_weight_matrix()
target_data = network.create_target_weights(input_data)
# Calculating first output
first_output = network(input_data)
# Getting the second output by initializing a new net with the weights of the original net.
# FixMe: Is this correct? I Think it should be the same function thus the same network
net_copy = copy.deepcopy(network)
net_copy.apply_weights(first_output)
input_data_2 = net_copy.input_weight_matrix()
@@ -60,50 +47,33 @@ def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> b
# Calculating second output
second_output = network(input_data_2)
# Perform the Check:
check_abs_within_epsilon = all(epsilon > abs(input_data - second_output))
# FIXME: This is wrong, is it?
# check_smaller_epsilon = all(epsilon > second_output)
# check_greater_epsilon = all(-epsilon < second_output)
return True if check_abs_within_epsilon else False
def is_weak_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
result = overall_fixpoint_test(network, epsilon, input_data)
return result
# Perform the Check: all(epsilon > abs(input_data - second_output))
check_abs_within_epsilon = np.allclose(target_data.detach().numpy(), second_output.detach().numpy(),
rtol=0, atol=epsilon)
return check_abs_within_epsilon
def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
id_functions = id_functions or None
zero_epsilon = pow(10, -5)
epsilon = pow(10, -3)
for i in range(len(nets)):
net = nets[i]
input_data = net.input_weight_matrix()
if is_divergent(nets[i]):
fixpoint_counter["divergent"] += 1
nets[i].is_fixpoint = "divergent"
elif is_identity_function(nets[i], zero_epsilon):
elif is_identity_function(nets[i]): # is default value
fixpoint_counter["identity_func"] += 1
nets[i].is_fixpoint = "identity_func"
id_functions.append(nets[i])
elif is_zero_fixpoint(nets[i], input_data, zero_epsilon):
elif is_zero_fixpoint(nets[i]):
fixpoint_counter["fix_zero"] += 1
nets[i].is_fixpoint = "fix_zero"
elif is_weak_fixpoint(nets[i], input_data, epsilon):
fixpoint_counter["fix_weak"] += 1
nets[i].is_fixpoint = "fix_weak"
elif is_secondary_fixpoint(nets[i], input_data, zero_epsilon):
elif is_secondary_fixpoint(nets[i]):
fixpoint_counter["fix_sec"] += 1
nets[i].is_fixpoint = "fix_sec"
else:
fixpoint_counter["other_func"] += 1
nets[i].is_fixpoint = "other_func"
return id_functions

30
main.py
View File

@@ -1,5 +1,4 @@
from experiments import run_ST_experiment, run_SA_experiment, run_soup_experiment, run_mixed_experiment, \
run_robustness_experiment
from experiments import *
import random
@@ -19,17 +18,20 @@ def run_experiments(run_ST, run_SA, run_soup, run_mixed, run_robustness):
if run_soup:
print(f"\n Running the soup experiment:")
run_soup_experiment(soup_population_size, soup_attack_chance, NET_INPUT_SIZE, soup_net_hidden_size,
NET_OUT_SIZE, soup_net_learning_rate, soup_epochs, soup_log_step_size, soup_runs, soup_runs_name,
soup_name_hash, soup_ST_steps, soup_train_nets)
NET_OUT_SIZE, soup_net_learning_rate, soup_epochs, soup_log_step_size, soup_runs,
soup_runs_name, soup_name_hash, soup_ST_steps, soup_train_nets)
if run_mixed:
print(f"\n Running the mixed experiment:")
run_mixed_experiment(mixed_population_size, NET_INPUT_SIZE, mixed_net_hidden_size, NET_OUT_SIZE,
mixed_net_learning_rate, mixed_train_nets, mixed_epochs, mixed_SA_steps,
mixed_ST_steps_between_SA, mixed_log_step_size, mixed_name_hash, mixed_total_runs, mixed_runs_name)
mixed_ST_steps_between_SA, mixed_log_step_size, mixed_name_hash, mixed_total_runs,
mixed_runs_name)
if run_robustness:
print(f"Running the robustness experiment:")
run_robustness_experiment(rob_population_size, rob_log_step_size, NET_INPUT_SIZE, rob_net_hidden_size,
NET_OUT_SIZE, rob_net_learning_rate, rob_ST_steps, rob_runs, rob_runs_name, rob_name_hash)
NET_OUT_SIZE, rob_net_learning_rate, rob_ST_steps, rob_runs, rob_runs_name,
rob_name_hash)
if not run_ST and not run_SA and not run_soup and not run_mixed and not run_robustness:
print(f"No experiments to be run.")
@@ -38,9 +40,13 @@ if __name__ == '__main__':
# Constants:
NET_INPUT_SIZE = 4
NET_OUT_SIZE = 1
run_ST_experiment_bool = True
run_SA_experiment_bool = False
run_soup_experiment_bool = False
run_mixed_experiment_bool = False
run_robustness_bool = False
""" ------------------------------------- Self-training (ST) experiment ------------------------------------- """
run_ST_experiment_bool = False
# Define number of runs & name:
ST_runs = 1
@@ -57,9 +63,6 @@ if __name__ == '__main__':
ST_name_hash = random.getrandbits(32)
""" ----------------------------------- Self-application (SA) experiment ----------------------------------- """
run_SA_experiment_bool = False
# Define number of runs, name, etc.:
SA_runs_name = "test-17"
SA_runs = 2
@@ -81,9 +84,6 @@ if __name__ == '__main__':
SA_name_hash = random.getrandbits(32)
""" -------------------------------------------- Soup experiment -------------------------------------------- """
run_soup_experiment_bool = False
# Define number of runs, name, etc.:
soup_runs = 1
soup_runs_name = "test-16"
@@ -107,8 +107,6 @@ if __name__ == '__main__':
""" ------------------------------------------- Mixed experiment -------------------------------------------- """
run_mixed_experiment_bool = False
# Define number of runs, name, etc.:
mixed_runs_name = "test-17"
mixed_total_runs = 2
@@ -132,8 +130,6 @@ if __name__ == '__main__':
mixed_name_hash = random.getrandbits(32)
""" ----------------------------------------- Robustness experiment ----------------------------------------- """
run_robustness_bool = True
# Define number of runs & name:
rob_runs = 3
rob_runs_name = "test-07"

30
visualization.py
View File

@@ -1,6 +1,5 @@
from pathlib import Path
from tokenize import String
from typing import List, Dict
from typing import List, Dict, Union
from tqdm import tqdm
import matplotlib.pyplot as plt
@@ -19,7 +18,7 @@ def plot_output(output):
plt.show()
def plot_loss(loss_array, directory, batch_size=1):
def plot_loss(loss_array, directory: Union[str, Path], batch_size=1):
""" Plotting the evolution of the loss function."""
fig = plt.figure()
@@ -41,8 +40,8 @@ def plot_loss(loss_array, directory, batch_size=1):
plt.clf()
def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory: String, learning_rate: float,
exp_details: String, source_check=None):
def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory: Union[str, Path], learning_rate: float,
exp_details: str, source_check=None):
""" Plotting the number of fixpoints in a barchart. """
fig = plt.figure()
@@ -73,8 +72,8 @@ def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory:
plt.clf()
def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend, exp_name="experiment", is_trained="",
batch_size=1):
def plot_3d(matrices_weights_history, directory: Union[str, Path], population_size, z_axis_legend,
exp_name="experiment", is_trained="", batch_size=1):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@@ -109,7 +108,10 @@ def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend,
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")
if z_axis_legend == "Self-application":
trained = mpatches.Patch(color="white", label=f"Trained: true") if is_trained == "_trained" else mpatches.Patch(color="white", label=f"Trained: false")
if is_trained == '_trained':
trained = mpatches.Patch(color="white", label=f"Trained: true")
else:
trained = mpatches.Patch(color="white", label=f"Trained: false")
ax.legend(handles=[steps, population_size, trained])
else:
ax.legend(handles=[steps, population_size])
@@ -134,7 +136,7 @@ def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend,
plt.show()
def plot_3d_self_train(nets_array: List, exp_name: String, directory: String, batch_size: int):
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int):
""" Plotting the evolution of the weights in a 3D space when doing self training. """
matrices_weights_history = []
@@ -150,7 +152,7 @@ def plot_3d_self_train(nets_array: List, exp_name: String, directory: String, ba
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size)
def plot_3d_self_application(nets_array: List, exp_name: String, directory_name: String, batch_size: int) -> None:
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None:
""" Plotting the evolution of the weights in a 3D space when doing self application. """
matrices_weights_history = []
@@ -171,7 +173,7 @@ def plot_3d_self_application(nets_array: List, exp_name: String, directory_name:
plot_3d(matrices_weights_history, directory_name, len(nets_array), z_axis_legend, exp_name, is_trained, batch_size)
def plot_3d_soup(nets_list, exp_name, directory):
def plot_3d_soup(nets_list, exp_name, directory: Union[str, Path]):
""" Plotting the evolution of the weights in a 3D space for the soup environment. """
# This batch size is not relevant for soups. To not affect the number of epochs shown in the 3D plot,
@@ -182,7 +184,7 @@ def plot_3d_soup(nets_list, exp_name, directory):
def line_chart_fixpoints(fixpoint_counters_history: list, epochs: int, ST_steps_between_SA: int,
SA_steps, directory: String, population_size: int):
SA_steps, directory: Union[str, Path], population_size: int):
""" Plotting the percentage of fixpoints after each iteration of SA & ST steps. """
fig = plt.figure()
@@ -211,7 +213,7 @@ def line_chart_fixpoints(fixpoint_counters_history: list, epochs: int, ST_steps_
plt.clf()
def box_plot(data, directory, population_size):
def box_plot(data, directory: Union[str, Path], population_size):
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 7))
# ax = fig.add_axes([0, 0, 1, 1])
@@ -232,7 +234,7 @@ def box_plot(data, directory, population_size):
plt.clf()
def write_file(text, directory):
def write_file(text, directory: Union[str, Path]):
directory = Path(directory)
filepath = directory / 'experiment.txt'
with filepath.open('w+') as f: