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application losses

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steffen-illium
2021-05-23 10:33:54 +02:00
parent b1dc574f5b
commit 54590eb147
1 changed files with 51 additions and 43 deletions
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94
journal_robustness.py
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@@ -12,17 +12,18 @@ from sklearn.metrics import mean_squared_error as MSE
from journal_basins import mean_invariate_manhattan_distance as MIM
from functionalities_test import is_identity_function, is_zero_fixpoint, test_for_fixpoints, is_divergent
from network import Net
from torch.nn import functional as F
from visualization import plot_loss, bar_chart_fixpoints
def prng():
return random.random()
def generate_fixpoint_weights():
return torch.tensor([ [1.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],
[1.0], [0.0], [0.0], [0.0],
[1.0], [0.0]
], dtype=torch.float32)
def generate_perfekt_synthetic_fixpoint_weights():
return torch.tensor([[1.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],
[1.0], [0.0], [0.0], [0.0],
[1.0], [0.0]
], dtype=torch.float32)
class RobustnessComparisonExperiment:
@@ -39,7 +40,7 @@ class RobustnessComparisonExperiment:
network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
else:
network.state_dict()[layer_name][line_id][weight_id] = weight_value - noise
return network
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
@@ -53,7 +54,6 @@ class RobustnessComparisonExperiment:
self.epochs = epochs
self.ST_steps = st_steps
self.loss_history = []
self.nets = []
self.synthetic = synthetic
self.fixpoint_counters = {
"identity_func": 0,
@@ -68,7 +68,7 @@ class RobustnessComparisonExperiment:
self.directory.mkdir(parents=True, exist_ok=True)
self.id_functions = []
self.populate_environment()
self.nets = self.populate_environment()
self.count_fixpoints()
self.time_to_vergence, self.time_as_fixpoint = self.test_robustness()
@@ -76,64 +76,72 @@ class RobustnessComparisonExperiment:
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
nets = []
for i in loop_population_size:
loop_population_size.set_description("Populating experiment %s" % i)
if self.synthetic:
''' Either use perfect / hand-constructed fixpoint ... '''
net_name = f"net_{str(i)}_synthetic"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
net.apply_weights(generate_fixpoint_weights())
net.apply_weights(generate_perfekt_synthetic_fixpoint_weights())
else:
''' .. or use natural approach to train fixpoints from random initialisation. '''
net_name = f"net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.epochs):
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
self.nets.append(net)
net.self_train(self.ST_steps, self.log_step_size, self.net_learning_rate)
nets.append(net)
return nets
def test_robustness(self, print_it=True):
time_to_vergence = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
time_as_fixpoint = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
avg_time_to_vergence = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
avg_time_as_fixpoint = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
avg_loss_per_application = [[0 for _ in range(10)] for _ in range(len(self.id_functions))]
noise_range = range(10)
row_headers = []
for i, fixpoint in enumerate(self.id_functions):
row_headers.append(fixpoint.name)
for noise_level in noise_range:
application_losses = []
for seed in range(10):
for noise_level in noise_range:
application_losses = []
clone = Net(fixpoint.input_size, fixpoint.hidden_size, fixpoint.out_size,
f"{fixpoint.name}_clone_noise10e-{noise_level}")
clone.load_state_dict(copy.deepcopy(fixpoint.state_dict()))
rand_noise = prng() * pow(10, -noise_level)
clone = self.apply_noise(clone, rand_noise)
while not is_zero_fixpoint(clone) and not is_divergent(clone):
if is_identity_function(clone):
avg_time_as_fixpoint[i][noise_level] += 1
# -> before
clone_weight_pre_application = clone.input_weight_matrix()
target_data_pre_application = clone.create_target_weights(clone_weight_pre_application)
clone.self_application(1, self.log_step_size)
avg_time_to_vergence[i][noise_level] += 1
# -> after
clone_weight_post_application = clone.input_weight_matrix()
target_data_post_application = clone.create_target_weights(clone_weight_post_application)
application_losses.append(F.l1_loss(target_data_pre_application, target_data_post_application))
clone = Net(fixpoint.input_size, fixpoint.hidden_size, fixpoint.out_size,
f"{fixpoint.name}_clone_noise10e-{noise_level}")
clone.load_state_dict(copy.deepcopy(fixpoint.state_dict()))
rand_noise = prng() * pow(10, -noise_level)
clone = self.apply_noise(clone, rand_noise)
while not is_zero_fixpoint(clone) and not is_divergent(clone):
if is_identity_function(clone):
time_as_fixpoint[i][noise_level] += 1
# Todo: what kind of comparison between application? -> before
clone.self_application(1, self.log_step_size)
time_to_vergence[i][noise_level] += 1
# -> after
if print_it:
col_headers = [str(f"10e-{d}") for d in noise_range]
print(f"\nAppplications steps until divergence / zero: ")
print(tabulate(time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
print(f"\nTime as fixpoint: ")
print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
return time_as_fixpoint, time_to_vergence
print(f"\nAppplications steps until divergence / zero: ")
print(tabulate(avg_time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
print(f"\nTime as fixpoint: ")
print(tabulate(avg_time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
return avg_time_as_fixpoint, avg_time_to_vergence
def count_fixpoints(self):