fixed soup_basin experiment
This commit is contained in:
ru43zex
@@ -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):
|
||||
@@ -65,13 +66,14 @@ def distance_from_parent(nets, distance="MIM", print_it=True):
|
||||
|
||||
for dist in distance_range:
|
||||
for idx, clone in enumerate(clones):
|
||||
clone_weights = clone.create_target_weights(clone.input_weight_matrix())
|
||||
clone_weights = clone.create_target_weights(clone.input_weight_matrix())
|
||||
if distance in ["MSE"]:
|
||||
matrix[idx][dist] = MSE(parent_weights, clone_weights) < pow(10, -dist)
|
||||
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}]:")
|
||||
@@ -80,9 +82,10 @@ def distance_from_parent(nets, distance="MIM", print_it=True):
|
||||
print(tabulate(matrix, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
|
||||
|
||||
list_of_matrices.append(matrix)
|
||||
|
||||
|
||||
return list_of_matrices
|
||||
|
||||
|
||||
class SpawnExperiment:
|
||||
|
||||
@staticmethod
|
||||
@@ -92,12 +95,12 @@ 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:
|
||||
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,
|
||||
@@ -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):
|
||||
@@ -155,7 +160,7 @@ class SpawnExperiment:
|
||||
net.start_time = self.ST_steps - 150
|
||||
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")
|
||||
|
||||
@@ -171,7 +176,7 @@ class SpawnExperiment:
|
||||
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)
|
||||
@@ -182,7 +187,7 @@ class SpawnExperiment:
|
||||
for _ in range(self.epochs - 1):
|
||||
for _ in range(self.ST_steps):
|
||||
clone.self_train(1, self.log_step_size, self.net_learning_rate)
|
||||
|
||||
|
||||
# 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)
|
||||
@@ -192,23 +197,24 @@ class SpawnExperiment:
|
||||
# .. 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."
|
||||
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]
|
||||
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")
|
||||
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
|
||||
|
||||
@@ -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,
|
||||
@@ -272,4 +278,4 @@ if __name__ == "__main__":
|
||||
# Catplot (either kind="point" or "box") that shows before-after training distances to parent
|
||||
mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance')
|
||||
sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False)
|
||||
plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")
|
||||
plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")
|
||||
|
||||
Reference in New Issue
Block a user