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fixed soup_basin experiment

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This commit is contained in:
ru43zex
2021-06-05 17:44:37 +03:00
parent 0320957b85
commit 2077d800ae
6 changed files with 121 additions and 91 deletions
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2
experiments/mixed_setting_exp.py
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@ -95,7 +95,7 @@ class MixedSettingExperiment:
# 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)
plot_3d_self_train(self.nets, exp_name, self.directory_name, irrelevant_batch_size, True)
def count_fixpoints(self):
exp_details = f"SA steps: {self.SA_steps}; ST steps: {self.ST_steps_between_SA}"

3
experiments/soup_exp.py
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@ -88,8 +88,7 @@ class SoupExperiment:
# 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)
fixpoints_percentage = round(self.fixpoint_counters["identity_func"] / self.population_size, 1)
self.fixpoint_counters_history.append(fixpoints_percentage)
# Resetting the fixpoint counter. Last iteration not to be reset -

26
journal_basins.py
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@ -17,13 +17,14 @@ import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
def prng():
return random.random()
def l1(tup):
a, b = tup
return abs(a-b)
return abs(a - b)
def mean_invariate_manhattan_distance(x, y):
@ -71,7 +72,8 @@ def distance_from_parent(nets, distance="MIM", print_it=True):
elif distance in ["MAE"]:
matrix[idx][dist] = MAE(parent_weights, clone_weights) < pow(10, -dist)
elif distance in ["MIM"]:
matrix[idx][dist] = mean_invariate_manhattan_distance(parent_weights, clone_weights) < pow(10, -dist)
matrix[idx][dist] = mean_invariate_manhattan_distance(parent_weights, clone_weights) < pow(10,
-dist)
if print_it:
print(f"\nDistances from parent {parent.name} [{distance}]:")
@ -83,6 +85,7 @@ def distance_from_parent(nets, distance="MIM", print_it=True):
return list_of_matrices
class SpawnExperiment:
@staticmethod
@ -92,7 +95,7 @@ class SpawnExperiment:
for layer_id, layer_name in enumerate(network.state_dict()):
for line_id, line_values in enumerate(network.state_dict()[layer_name]):
for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]):
#network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
# network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
if prng() < 0.5:
network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
else:
@ -144,7 +147,9 @@ class SpawnExperiment:
def spawn_and_continue(self, number_clones: int = None):
number_clones = number_clones or self.nr_clones
df = pd.DataFrame(columns=['parent', 'MAE_pre','MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise', 'status_post'])
df = pd.DataFrame(
columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
'status_post'])
# For every initial net {i} after populating (that is fixpoint after first epoch);
for i in range(self.population_size):
@ -198,7 +203,8 @@ class SpawnExperiment:
f"\nMIM({i},{j}): {MIM_post}\n")
self.nets.append(clone)
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise, clone.is_fixpoint]
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):
@ -206,9 +212,9 @@ class SpawnExperiment:
net.self_train(1, self.log_step_size, self.net_learning_rate)
net_weights_after = net.create_target_weights(net.input_weight_matrix())
print(f"Parent net's distance to original position."
f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
self.df = df
@ -222,11 +228,11 @@ class SpawnExperiment:
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory)
def save(self):
pickle.dump(self, open(f"{self.directory}/experiment_pickle.p", "wb"))
print(f"\nSaved experiment to {self.directory}.")
if __name__ == "__main__":
NET_INPUT_SIZE = 4
@ -248,7 +254,7 @@ if __name__ == "__main__":
print(f"Running the Spawn experiment:")
exp_list = []
for noise_factor in range(2,5):
for noise_factor in range(2, 5):
exp = SpawnExperiment(
population_size=ST_population_size,
log_step_size=ST_log_step_size,

147
journal_soup_basins.py
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@ -124,11 +124,13 @@ class SoupSpawnExperiment:
# Populating environment & evolving entities
self.nets = []
self.id_functions = []
self.clone_soup = []
self.populate_environment()
self.evolve()
self.spawn_and_continue()
self.weights_evolution_3d_experiment()
self.weights_evolution_3d_experiment(self.nets, "parents")
self.weights_evolution_3d_experiment(self.clone_soup, "clones")
# self.visualize_loss()
self.distance_matrix = distance_matrix(self.nets, print_it=False)
self.parent_clone_distances = distance_from_parent(self.nets, print_it=False)
@ -140,27 +142,35 @@ class SoupSpawnExperiment:
for i in loop_population_size:
loop_population_size.set_description("Populating experiment %s" % i)
net_name = f"soup_net_{str(i)}"
net_name = f"parent_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):
net.self_train(1, self.log_step_size, self.net_learning_rate)
self.nets.append(net)
def evolve(self):
loop_epochs = tqdm(range(self.epochs))
if is_identity_function(net):
self.id_functions.append(net)
def evolve(self, population):
print(f"Clone soup has a population of {len(population)} networks")
loop_epochs = tqdm(range(self.epochs-1))
for i in loop_epochs:
loop_epochs.set_description("Evolving soup %s" % i)
loop_epochs.set_description("\nEvolving clone soup %s" % i)
# A network attacking another network with a given percentage
if random.randint(1, 100) <= self.attack_chance:
random_net1, random_net2 = random.sample(range(self.population_size), 2)
random_net1 = self.nets[random_net1]
random_net2 = self.nets[random_net2]
random_net1, random_net2 = random.sample(range(len(population)), 2)
random_net1 = population[random_net1]
random_net2 = 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.nets[j]
for j in range(len(population)):
net = population[j]
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
@ -172,8 +182,10 @@ class SoupSpawnExperiment:
columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
'status_post'])
# MAE_pre, MSE_pre, MIM_pre = 0, 0, 0
# For every initial net {i} after populating (that is fixpoint after first epoch);
for i in range(self.population_size):
for i in range(len(self.id_functions)):
net = self.nets[i]
# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to
# to see full trajectory (but the clones will be very hard to see).
@ -182,66 +194,73 @@ class SoupSpawnExperiment:
net_input_data = net.input_weight_matrix()
net_target_data = net.create_target_weights(net_input_data)
if is_identity_function(net):
print(f"\nNet {i} is fixpoint")
print(f"\nNet {i} is fixpoint")
# Clone the fixpoint x times and add (+-)self.noise to weight-sets randomly;
# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
# To make sure PCA will plot the same trajectory up until this point, we clone the
# parent-net's weight history as well.
for j in range(number_clones):
clone = Net(net.input_size, net.hidden_size, net.out_size,
f"ST_net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
clone.load_state_dict(copy.deepcopy(net.state_dict()))
rand_noise = prng() * self.noise
clone = self.apply_noise(clone, rand_noise)
clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
clone.number_trained = copy.deepcopy(net.number_trained)
# Clone the fixpoint x times and add (+-)self.noise to weight-sets randomly;
# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
# To make sure PCA will plot the same trajectory up until this point, we clone the
# parent-net's weight history as well.
for j in range(number_clones):
clone = Net(net.input_size, net.hidden_size, net.out_size,
f"net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
clone.load_state_dict(copy.deepcopy(net.state_dict()))
rand_noise = prng() * self.noise
clone = self.apply_noise(clone, rand_noise)
clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
clone.number_trained = copy.deepcopy(net.number_trained)
# Pre Training distances (after noise application of course)
clone_pre_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_pre = MAE(net_target_data, clone_pre_weights)
MSE_pre = MSE(net_target_data, clone_pre_weights)
MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights)
# Pre Training distances (after noise application of course)
clone_pre_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_pre = MAE(net_target_data, clone_pre_weights)
MSE_pre = MSE(net_target_data, clone_pre_weights)
MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights)
# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
clone.self_train(1, self.log_step_size, self.net_learning_rate)
net.children.append(clone)
self.clone_soup.append(clone)
# Post Training distances for comparison
clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_post = MAE(net_target_data, clone_post_weights)
MSE_post = MSE(net_target_data, clone_post_weights)
MIM_post = mean_invariate_manhattan_distance(net_target_data, clone_post_weights)
self.evolve(self.clone_soup)
# .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
test_status(clone)
if is_identity_function(clone):
print(f"Clone {j} (of net_{i}) is fixpoint."
f"\nMSE({i},{j}): {MSE_post}"
f"\nMAE({i},{j}): {MAE_post}"
f"\nMIM({i},{j}): {MIM_post}\n")
self.nets.append(clone)
for i in range(len(self.id_functions)):
net = self.nets[i]
net_input_data = net.input_weight_matrix()
net_target_data = net.create_target_weights(net_input_data)
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
clone.is_fixpoint]
for j in range(len(net.children)):
clone = net.children[j]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
net_weights_after = net.create_target_weights(net.input_weight_matrix())
print(f"Parent net's distance to original position."
f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
# Post Training distances for comparison
clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_post = MAE(net_target_data, clone_post_weights)
MSE_post = MSE(net_target_data, clone_post_weights)
MIM_post = mean_invariate_manhattan_distance(net_target_data, clone_post_weights)
# .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
test_status(clone)
if is_identity_function(clone):
print(f"Clone {j} (of net_{i}) is fixpoint."
f"\nMSE({i},{j}): {MSE_post}"
f"\nMAE({i},{j}): {MAE_post}"
f"\nMIM({i},{j}): {MIM_post}\n")
self.nets.append(clone)
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
net_weights_after = net.create_target_weights(net.input_weight_matrix())
print(f"Parent net's distance to original position."
f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
self.df = df
def weights_evolution_3d_experiment(self):
exp_name = f"soup_basins_{str(len(self.nets))}_nets_3d_weights_PCA"
return plot_3d_soup(self.nets, exp_name, self.directory)
def weights_evolution_3d_experiment(self, nets_population, suffix):
exp_name = f"soup_basins_{str(len(self.nets))}_nets_3d_weights_PCA_{suffix}"
return plot_3d_soup(nets_population, exp_name, self.directory)
def visualize_loss(self):
for i in range(len(self.nets)):
@ -262,12 +281,12 @@ if __name__ == "__main__":
# Define number of runs & name:
ST_runs = 1
ST_runs_name = "test-27"
soup_ST_steps = 2500
soup_ST_steps = 1500
soup_epochs = 2
soup_log_step_size = 10
# Define number of networks & their architecture
nr_clones = 15
nr_clones = 2
soup_population_size = 2
soup_net_hidden_size = 2
soup_net_learning_rate = 0.04

3
network.py
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@ -48,7 +48,10 @@ class Net(nn.Module):
def __init__(self, i_size: int, h_size: int, o_size: int, name=None, start_time=1) -> None:
super().__init__()
self.start_time = start_time
self.name = name
self.children = []
self.input_size = i_size
self.hidden_size = h_size
self.out_size = o_size

13
visualization.py
View File

@ -73,7 +73,7 @@ def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory:
def plot_3d(matrices_weights_history, directory: Union[str, Path], population_size, z_axis_legend,
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=False):
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=False, nets_array=None):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -134,7 +134,10 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size)
ax.plot3D(xdata, ydata, zdata, label=f"net {i}")
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=7)
if "parent" in nets_array[i].name:
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3, c="b")
else:
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3)
steps = mpatches.Patch(color="white", label=f"{z_axis_legend}: {len(matrices_weights_history)} steps")
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")
@ -165,7 +168,7 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
else:
plt.savefig(str(filepath))
plt.show()
# plt.show()
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int, plot_pca_together: bool):
@ -177,12 +180,12 @@ def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Pa
for i in loop_nets_array:
loop_nets_array.set_description("Creating ST weights history %s" % i)
matrices_weights_history.append( (nets_array[i].s_train_weights_history, nets_array[i].start_time) )
matrices_weights_history.append((nets_array[i].s_train_weights_history, nets_array[i].start_time))
z_axis_legend = "epochs"
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size,
plot_pca_together=plot_pca_together)
plot_pca_together=plot_pca_together, nets_array=nets_array)
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None: