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Added pickle save() function for SpawnExperiment, updated README, set

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plot-pca-all false on default, just True for SpawnExp for now.
This commit is contained in:
Maximilian Zorn
2021-05-21 16:37:27 +02:00
parent 1e8ccd2b8b
commit bcfe5807a7
3 changed files with 51 additions and 41 deletions
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53
journal_basins.py
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@@ -1,5 +1,6 @@
import os
from pathlib import Path
import pickle
from tqdm import tqdm
import random
@@ -51,29 +52,33 @@ def distance_matrix(nets, distance="MIM", print_it=True):
def distance_from_parent(nets, distance="MIM", print_it=True):
list_of_matrices = []
parents = list(filter(lambda x: "clone" not in x.name and is_identity_function(x), nets))
distance_range = range(10)
for parent in parents:
parent_weights = parent.create_target_weights(parent.input_weight_matrix())
clones = list(filter(lambda y: parent.name in y.name and parent.name != y.name, nets))
matrix = [[0 for _ in distance_range] for _ in range(len(clones))]
parent_weights = parent.create_target_weights(parent.input_weight_matrix())
clones = list(filter(lambda y: parent.name in y.name and parent.name != y.name, nets))
matrix = [[0 for _ in distance_range] for _ in range(len(clones))]
for dist in distance_range:
for idx, clone in enumerate(clones):
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)
if print_it:
print(f"\nDistances from parent {parent.name} [{distance}]:")
col_headers = [str(f"10e-{d}") for d in distance_range]
row_headers = [str(f"clone_{i}") for i in range(len(clones))]
print(tabulate(matrix, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
for dist in distance_range:
for idx, clone in enumerate(clones):
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)
if print_it:
print(f"\nDistances from parent {parent.name} [{distance}]:")
col_headers = [str(f"10e-{d}") for d in distance_range]
row_headers = [str(f"clone_{i}") for i in range(len(clones))]
print(tabulate(matrix, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
list_of_matrices.append(matrix)
return matrix
return list_of_matrices
class SpawnExperiment:
@@ -115,8 +120,10 @@ class SpawnExperiment:
self.spawn_and_continue()
self.weights_evolution_3d_experiment()
# self.visualize_loss()
distance_matrix(self.nets)
distance_from_parent(self.nets)
self.distance_matrix = distance_matrix(self.nets)
self.parent_clone_distances = distance_from_parent(self.nets)
self.save()
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
@@ -184,7 +191,7 @@ class SpawnExperiment:
def weights_evolution_3d_experiment(self):
exp_name = f"ST_{str(len(self.nets))}_nets_3d_weights_PCA"
return plot_3d_self_train(self.nets, exp_name, self.directory, self.log_step_size)
return plot_3d_self_train(self.nets, exp_name, self.directory, self.log_step_size, plot_pca_together=True)
def visualize_loss(self):
for i in range(len(self.nets)):
@@ -193,6 +200,10 @@ class SpawnExperiment:
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