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journal_basins.py debugged

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steffen-illium
2021-05-16 11:30:34 +02:00
parent 5074100b71
commit 042188f15a
5 changed files with 89 additions and 75 deletions
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5
experiments/__init__.py
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@ -0,0 +1,5 @@
from mixed_setting_exp import run_mixed_experiment
from robustness_exp import run_robustness_experiment
from self_application_exp import run_SA_experiment
from self_train_exp import run_ST_experiment
from soup_exp import run_soup_experiment

1
experiments/robustness_exp.py
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@ -20,7 +20,6 @@ def add_noise(input_data, epsilon = pow(10, -5)):
return output
class RobustnessExperiment:
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
ST_steps, directory_name) -> None:

55
journal_basins.py
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@ -1,4 +1,6 @@
import os
from pathlib import Path
from tqdm import tqdm
import random
import copy
@ -14,16 +16,19 @@ from sklearn.metrics import mean_squared_error as MSE
def prng():
return random.random()
def l1(tup):
a, b = tup
return abs(a-b)
def mean_invariate_manhattan_distance(X,Y):
def mean_invariate_manhattan_distance(x, y):
# One of these one-liners that might be smart or really dumb. Goal is to find pairwise
# distances of ascending values, ie. sum (abs(min1_X-min1_Y), abs(min2_X-min2Y) ...) / mean.
# Idea was to find weight sets that have same values but just in different positions, that would
# make this distance 0.
return np.mean(list(map(l1, zip(sorted(X),sorted(Y)))))
return np.mean(list(map(l1, zip(sorted(x), sorted(y)))))
def distance_matrix(nets, distance="MIM", print_it=True):
matrix = [[0 for _ in range(len(nets))] for _ in range(len(nets))]
@ -43,6 +48,7 @@ def distance_matrix(nets, distance="MIM", print_it=True):
[print(row) for row in matrix]
return matrix
class SpawnExperiment:
@staticmethod
@ -61,7 +67,7 @@ class SpawnExperiment:
return network
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, ST_steps, noise, directory_name) -> None:
epochs, st_steps, noise, directory_name) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
@ -69,14 +75,14 @@ class SpawnExperiment:
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.epochs = epochs
self.ST_steps = ST_steps
self.ST_steps = st_steps
self.loss_history = []
self.nets = []
self.noise = noise or 10e-5
print("\nNOISE:", self.noise)
self.directory_name = directory_name
os.mkdir(self.directory_name)
self.directory = Path(directory_name)
self.directory.mkdir(parents=True, exist_ok=True)
self.populate_environment()
self.spawn_and_continue()
@ -93,14 +99,12 @@ class SpawnExperiment:
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.ST_steps):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
net.self_train(1, self.log_step_size, self.net_learning_rate)
#print(f"\nLast weight matrix (epoch: {self.epochs}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
# print(f"\nLast weight matrix (epoch: {self.epochs}):\n
# {net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
self.nets.append(net)
def spawn_and_continue(self, number_spawns: int = 5):
# For every initial net {i} after populating (that is fixpoint after first epoch);
for i in range(self.population_size):
@ -108,35 +112,37 @@ class SpawnExperiment:
net_input_data = net.input_weight_matrix()
net_target_data = net.create_target_weights(net_input_data)
if is_identity_function(net, net_input_data, net_target_data):
if is_identity_function(net):
print(f"\nNet {i} is fixpoint")
# print("\nNet weights before training\n", target_data)
# 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!
for j in range(number_spawns):
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 = 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)
# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) and add to nets for plotting;
# Then finish training each clone {j} (for remaining epoch-1 * ST_steps)
# and add to nets for plotting;
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
clone.self_train(1, self.log_step_size, self.net_learning_rate)
# print(f"clone {j} last weights: {target_data}, noise {noise}")
if is_identity_function(clone):
input_data = clone.input_weight_matrix()
target_data = clone.create_target_weights(input_data)
clone.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
#print(f"clone {j} last weights: {target_data}, noise {noise}")
if is_identity_function(clone, input_data, target_data):
print(f"Clone {j} (of net_{i}) is fixpoint. \nMSE(j,i): {MSE(net_target_data, target_data)}, \nMAE(j,i): {MAE(net_target_data, target_data)}\n")
print(f"Clone {j} (of net_{i}) is fixpoint. \nMSE(j,i): "
f"{MSE(net_target_data, target_data)}, \nMAE(j,i): {MAE(net_target_data, target_data)}\n")
self.nets.append(clone)
# 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):
input_data = net.input_weight_matrix()
target_data = net.create_target_weights(input_data)
net.self_train(1, self.log_step_size, self.net_learning_rate, input_data, target_data)
net.self_train(1, self.log_step_size, self.net_learning_rate)
# print("\nNet weights after training \n", target_data)
else:
@ -144,13 +150,14 @@ 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_name, self.log_step_size)
return plot_3d_self_train(self.nets, exp_name, self.directory.name, self.log_step_size)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory_name)
plot_loss(self.loss_history, self.directory.name)
if __name__ == "__main__":
@ -180,7 +187,7 @@ if __name__=="__main__":
net_out_size=NET_OUT_SIZE,
net_learning_rate=ST_net_learning_rate,
epochs=ST_epochs,
ST_steps=ST_steps,
st_steps=ST_steps,
noise=pow(10, -noise_factor),
directory_name=f"./experiments/spawn_basin/{ST_name_hash}_10e-{noise_factor}"
)

15
visualization.py
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@ -1,7 +1,7 @@
from pathlib import Path
from tokenize import String
from typing import List, Dict
import numpy
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
@ -120,14 +120,17 @@ def plot_3d(matrices_weights_history, folder_name, population_size, z_axis_legen
ax.set_ylabel("PCA Y")
ax.set_zlabel(f"Epochs")
filepath = f"./{folder_name}"
filename = f"{filepath}/{exp_name}{is_trained}.png"
if os.path.isfile(filename):
# FIXME: Replace this kind of operation with pathlib.Path() object interactions
folder = Path(folder_name)
folder.mkdir(parents=True, exist_ok=True)
filename = f"{exp_name}{is_trained}.png"
filepath = folder / filename
if filepath.exists():
letters = string.ascii_lowercase
random_letters = ''.join(random.choice(letters) for _ in range(5))
plt.savefig(f"{filename}_{random_letters}")
plt.savefig(f"{filepath.stem}_{random_letters}.png")
else:
plt.savefig(f"{filename}")
plt.savefig(str(filepath))
plt.show()
#plt.clf()