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

fixed soup_basin experiment

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

147
journal_soup_basins.py
View File

@ -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